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new AddTask macro: configures tender+clusterizer+cluster maker. First version. Adjust...
[u/mrichter/AliRoot.git] / PWGJE / UserTasks / AliAnalysisTaskPID.cxx
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e131b05f 1#include "TChain.h"
2#include "TFile.h"
3#include "TF1.h"
4#include "TAxis.h"
5#include "TProfile.h"
6#include "TRandom3.h"
7#include "TFitResultPtr.h"
8#include "TFitResult.h"
9
10#include "AliMCParticle.h"
11
12#include "AliAnalysisTask.h"
13#include "AliAnalysisManager.h"
14
15#include "AliESDEvent.h"
16#include "AliMCEvent.h"
17#include "AliESDInputHandler.h"
18#include "AliInputEventHandler.h"
19
20#include "AliVVertex.h"
21#include "AliAnalysisFilter.h"
22#include "AliPID.h"
23#include "AliPIDCombined.h"
24#include "AliPIDResponse.h"
25#include "AliTPCPIDResponse.h"
26
27#include "AliAnalysisTaskPID.h"
28
29/*
30This task collects PID output from different detectors.
31Only tracks fulfilling some standard quality cuts are taken into account.
32At the moment, only data from TPC and TOF is collected. But in future,
33data from e.g. HMPID is also foreseen.
34
35Contact: bhess@cern.ch
36*/
37
38ClassImp(AliAnalysisTaskPID)
39
77324970 40const Int_t AliAnalysisTaskPID::fgkNumJetAxes = 3; // Number of additional axes for jets
1f515a9d 41const Double_t AliAnalysisTaskPID::fgkEpsilon = 1e-8; // Double_t threshold above zero
77324970
CKB		 42const Int_t AliAnalysisTaskPID::fgkMaxNumGenEntries = 500; // Maximum number of generated detector responses per track and delta(Prime) and associated species
43
1f515a9d 44const Double_t AliAnalysisTaskPID::fgkOneOverSqrt2 = 0.707106781186547462; // = 1. / TMath::Sqrt2();
77324970
CKB		 45
46const Double_t AliAnalysisTaskPID::fgkSigmaReferenceForTransitionPars = 0.05; // Reference sigma chosen to calculate transition
1f515a9d 47
e131b05f 48//________________________________________________________________________
49AliAnalysisTaskPID::AliAnalysisTaskPID()
50 : AliAnalysisTaskPIDV0base()
51 , fPIDcombined(new AliPIDCombined())
52 , fInputFromOtherTask(kFALSE)
9e95a906 53 , fDoPID(kTRUE)
54 , fDoEfficiency(kTRUE)
55 , fDoPtResolution(kTRUE)
e131b05f 56 , fStoreCentralityPercentile(kFALSE)
57 , fStoreAdditionalJetInformation(kFALSE)
58 , fTakeIntoAccountMuons(kFALSE)
59 , fUseITS(kFALSE)
60 , fUseTOF(kFALSE)
61 , fUsePriors(kFALSE)
62 , fTPCDefaultPriors(kFALSE)
63 , fUseMCidForGeneration(kTRUE)
64 , fUseConvolutedGaus(kFALSE)
65 , fkConvolutedGausNPar(3)
66 , fAccuracyNonGaussianTail(1e-8)
67 , fkDeltaPrimeLowLimit(0.02)
68 , fkDeltaPrimeUpLimit(40.0)
69 , fConvolutedGausDeltaPrime(0x0)
77324970 70 , fTOFmode(1)
e131b05f 71 , fEtaAbsCutLow(0.0)
72 , fEtaAbsCutUp(0.9)
73 , fDoAnySystematicStudiesOnTheExpectedSignal(kFALSE)
21b0adb1 74 , fSystematicScalingSplinesThreshold(50.)
75 , fSystematicScalingSplinesBelowThreshold(1.0)
76 , fSystematicScalingSplinesAboveThreshold(1.0)
e131b05f 77 , fSystematicScalingEtaCorrectionMomentumThr(0.35)
78 , fSystematicScalingEtaCorrectionLowMomenta(1.0)
79 , fSystematicScalingEtaCorrectionHighMomenta(1.0)
5709c40a 80 , fSystematicScalingEtaSigmaParaThreshold(250.)
81 , fSystematicScalingEtaSigmaParaBelowThreshold(1.0)
82 , fSystematicScalingEtaSigmaParaAboveThreshold(1.0)
e131b05f 83 , fSystematicScalingMultCorrection(1.0)
84 , fCentralityEstimator("V0M")
85 , fhPIDdataAll(0x0)
86 , fhGenEl(0x0)
87 , fhGenKa(0x0)
88 , fhGenPi(0x0)
89 , fhGenMu(0x0)
90 , fhGenPr(0x0)
91 , fGenRespElDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
92 , fGenRespElDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
93 , fGenRespElDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
94 , fGenRespElDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
95 , fGenRespKaDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
96 , fGenRespKaDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
97 , fGenRespKaDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
98 , fGenRespKaDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
99 , fGenRespPiDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
100 , fGenRespPiDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
101 , fGenRespPiDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
102 , fGenRespPiDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
103 , fGenRespMuDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
104 , fGenRespMuDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
105 , fGenRespMuDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
106 , fGenRespMuDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
107 , fGenRespPrDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
108 , fGenRespPrDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
109 , fGenRespPrDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
110 , fGenRespPrDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
111 /*
112 , fGenRespElDeltaEl(new Double_t[fgkMaxNumGenEntries])
113 , fGenRespElDeltaKa(new Double_t[fgkMaxNumGenEntries])
114 , fGenRespElDeltaPi(new Double_t[fgkMaxNumGenEntries])
115 , fGenRespElDeltaPr(new Double_t[fgkMaxNumGenEntries])
116 , fGenRespKaDeltaEl(new Double_t[fgkMaxNumGenEntries])
117 , fGenRespKaDeltaKa(new Double_t[fgkMaxNumGenEntries])
118 , fGenRespKaDeltaPi(new Double_t[fgkMaxNumGenEntries])
119 , fGenRespKaDeltaPr(new Double_t[fgkMaxNumGenEntries])
120 , fGenRespPiDeltaEl(new Double_t[fgkMaxNumGenEntries])
121 , fGenRespPiDeltaKa(new Double_t[fgkMaxNumGenEntries])
122 , fGenRespPiDeltaPi(new Double_t[fgkMaxNumGenEntries])
123 , fGenRespPiDeltaPr(new Double_t[fgkMaxNumGenEntries])
124 , fGenRespMuDeltaEl(new Double_t[fgkMaxNumGenEntries])
125 , fGenRespMuDeltaKa(new Double_t[fgkMaxNumGenEntries])
126 , fGenRespMuDeltaPi(new Double_t[fgkMaxNumGenEntries])
127 , fGenRespMuDeltaPr(new Double_t[fgkMaxNumGenEntries])
128 , fGenRespPrDeltaEl(new Double_t[fgkMaxNumGenEntries])
129 , fGenRespPrDeltaKa(new Double_t[fgkMaxNumGenEntries])
130 , fGenRespPrDeltaPi(new Double_t[fgkMaxNumGenEntries])
131 , fGenRespPrDeltaPr(new Double_t[fgkMaxNumGenEntries])
132 */
133 , fhEventsProcessed(0x0)
4b4d71d4 134 , fhEventsTriggerSel(0x0)
135 , fhEventsTriggerSelVtxCut(0x0)
e131b05f 136 , fhSkippedTracksForSignalGeneration(0x0)
137 , fhMCgeneratedYieldsPrimaries(0x0)
138 , fh2FFJetPtRec(0x0)
139 , fh2FFJetPtGen(0x0)
140 , fh1Xsec(0x0)
141 , fh1Trials(0x0)
142 , fContainerEff(0x0)
143 , fOutputContainer(0x0)
9e95a906 144 , fPtResolutionContainer(0x0)
e131b05f 145{
146 // default Constructor
147
148 AliLog::SetClassDebugLevel("AliAnalysisTaskPID", AliLog::kInfo);
149
150 fConvolutedGausDeltaPrime = new TF1("convolutedGausDeltaPrime", this, &AliAnalysisTaskPID::ConvolutedGaus,
151 fkDeltaPrimeLowLimit, fkDeltaPrimeUpLimit,
152 fkConvolutedGausNPar, "AliAnalysisTaskPID", "ConvolutedGaus");
153
9d7ad2e4 154 // Set some arbitrary parameteres, such that the function call will not crash
155 // (although it should not be called with these parameters...)
156 fConvolutedGausDeltaPrime->SetParameter(0, 0);
157 fConvolutedGausDeltaPrime->SetParameter(1, 1);
158 fConvolutedGausDeltaPrime->SetParameter(2, 2);
159
160
e131b05f 161 // Initialisation of translation parameters is time consuming.
162 // Therefore, default values will only be initialised if they are really needed.
163 // Otherwise, it is left to the user to set the parameter properly.
164 fConvolutedGaussTransitionPars[0] = -999;
165 fConvolutedGaussTransitionPars[1] = -999;
166 fConvolutedGaussTransitionPars[2] = -999;
167
168 // Fraction histos
169 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
170 fFractionHists[i] = 0x0;
171 fFractionSysErrorHists[i] = 0x0;
9e95a906 172
173 fPtResolution[i] = 0x0;
e131b05f 174 }
175}
176
177//________________________________________________________________________
178AliAnalysisTaskPID::AliAnalysisTaskPID(const char *name)
179 : AliAnalysisTaskPIDV0base(name)
180 , fPIDcombined(new AliPIDCombined())
181 , fInputFromOtherTask(kFALSE)
9e95a906 182 , fDoPID(kTRUE)
183 , fDoEfficiency(kTRUE)
184 , fDoPtResolution(kTRUE)
e131b05f 185 , fStoreCentralityPercentile(kFALSE)
186 , fStoreAdditionalJetInformation(kFALSE)
187 , fTakeIntoAccountMuons(kFALSE)
188 , fUseITS(kFALSE)
189 , fUseTOF(kFALSE)
190 , fUsePriors(kFALSE)
191 , fTPCDefaultPriors(kFALSE)
192 , fUseMCidForGeneration(kTRUE)
193 , fUseConvolutedGaus(kFALSE)
194 , fkConvolutedGausNPar(3)
195 , fAccuracyNonGaussianTail(1e-8)
196 , fkDeltaPrimeLowLimit(0.02)
197 , fkDeltaPrimeUpLimit(40.0)
198 , fConvolutedGausDeltaPrime(0x0)
77324970 199 , fTOFmode(1)
e131b05f 200 , fEtaAbsCutLow(0.0)
201 , fEtaAbsCutUp(0.9)
202 , fDoAnySystematicStudiesOnTheExpectedSignal(kFALSE)
21b0adb1 203 , fSystematicScalingSplinesThreshold(50.)
204 , fSystematicScalingSplinesBelowThreshold(1.0)
205 , fSystematicScalingSplinesAboveThreshold(1.0)
e131b05f 206 , fSystematicScalingEtaCorrectionMomentumThr(0.35)
207 , fSystematicScalingEtaCorrectionLowMomenta(1.0)
208 , fSystematicScalingEtaCorrectionHighMomenta(1.0)
5709c40a 209 , fSystematicScalingEtaSigmaParaThreshold(250.)
210 , fSystematicScalingEtaSigmaParaBelowThreshold(1.0)
211 , fSystematicScalingEtaSigmaParaAboveThreshold(1.0)
e131b05f 212 , fSystematicScalingMultCorrection(1.0)
213 , fCentralityEstimator("V0M")
214 , fhPIDdataAll(0x0)
215 , fhGenEl(0x0)
216 , fhGenKa(0x0)
217 , fhGenPi(0x0)
218 , fhGenMu(0x0)
219 , fhGenPr(0x0)
220 , fGenRespElDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
221 , fGenRespElDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
222 , fGenRespElDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
223 , fGenRespElDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
224 , fGenRespKaDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
225 , fGenRespKaDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
226 , fGenRespKaDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
227 , fGenRespKaDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
228 , fGenRespPiDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
229 , fGenRespPiDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
230 , fGenRespPiDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
231 , fGenRespPiDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
232 , fGenRespMuDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
233 , fGenRespMuDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
234 , fGenRespMuDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
235 , fGenRespMuDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
236 , fGenRespPrDeltaPrimeEl(new Double_t[fgkMaxNumGenEntries])
237 , fGenRespPrDeltaPrimeKa(new Double_t[fgkMaxNumGenEntries])
238 , fGenRespPrDeltaPrimePi(new Double_t[fgkMaxNumGenEntries])
239 , fGenRespPrDeltaPrimePr(new Double_t[fgkMaxNumGenEntries])
240 /*
241 , fGenRespElDeltaEl(new Double_t[fgkMaxNumGenEntries])
242 , fGenRespElDeltaKa(new Double_t[fgkMaxNumGenEntries])
243 , fGenRespElDeltaPi(new Double_t[fgkMaxNumGenEntries])
244 , fGenRespElDeltaPr(new Double_t[fgkMaxNumGenEntries])
245 , fGenRespKaDeltaEl(new Double_t[fgkMaxNumGenEntries])
246 , fGenRespKaDeltaKa(new Double_t[fgkMaxNumGenEntries])
247 , fGenRespKaDeltaPi(new Double_t[fgkMaxNumGenEntries])
248 , fGenRespKaDeltaPr(new Double_t[fgkMaxNumGenEntries])
249 , fGenRespPiDeltaEl(new Double_t[fgkMaxNumGenEntries])
250 , fGenRespPiDeltaKa(new Double_t[fgkMaxNumGenEntries])
251 , fGenRespPiDeltaPi(new Double_t[fgkMaxNumGenEntries])
252 , fGenRespPiDeltaPr(new Double_t[fgkMaxNumGenEntries])
253 , fGenRespMuDeltaEl(new Double_t[fgkMaxNumGenEntries])
254 , fGenRespMuDeltaKa(new Double_t[fgkMaxNumGenEntries])
255 , fGenRespMuDeltaPi(new Double_t[fgkMaxNumGenEntries])
256 , fGenRespMuDeltaPr(new Double_t[fgkMaxNumGenEntries])
257 , fGenRespPrDeltaEl(new Double_t[fgkMaxNumGenEntries])
258 , fGenRespPrDeltaKa(new Double_t[fgkMaxNumGenEntries])
259 , fGenRespPrDeltaPi(new Double_t[fgkMaxNumGenEntries])
260 , fGenRespPrDeltaPr(new Double_t[fgkMaxNumGenEntries])
261 */
262 , fhEventsProcessed(0x0)
4b4d71d4 263 , fhEventsTriggerSel(0x0)
264 , fhEventsTriggerSelVtxCut(0x0)
e131b05f 265 , fhSkippedTracksForSignalGeneration(0x0)
266 , fhMCgeneratedYieldsPrimaries(0x0)
267 , fh2FFJetPtRec(0x0)
268 , fh2FFJetPtGen(0x0)
269 , fh1Xsec(0x0)
270 , fh1Trials(0x0)
271 , fContainerEff(0x0)
272 , fOutputContainer(0x0)
9e95a906 273 , fPtResolutionContainer(0x0)
e131b05f 274{
275 // Constructor
276
277 AliLog::SetClassDebugLevel("AliAnalysisTaskPID", AliLog::kInfo);
278
279 fConvolutedGausDeltaPrime = new TF1("convolutedGausDeltaPrime", this, &AliAnalysisTaskPID::ConvolutedGaus,
280 fkDeltaPrimeLowLimit, fkDeltaPrimeUpLimit,
281 fkConvolutedGausNPar, "AliAnalysisTaskPID", "ConvolutedGaus");
282
9d7ad2e4 283 // Set some arbitrary parameteres, such that the function call will not crash
284 // (although it should not be called with these parameters...)
285 fConvolutedGausDeltaPrime->SetParameter(0, 0);
286 fConvolutedGausDeltaPrime->SetParameter(1, 1);
287 fConvolutedGausDeltaPrime->SetParameter(2, 2);
288
289
e131b05f 290 // Initialisation of translation parameters is time consuming.
291 // Therefore, default values will only be initialised if they are really needed.
292 // Otherwise, it is left to the user to set the parameter properly.
293 fConvolutedGaussTransitionPars[0] = -999;
294 fConvolutedGaussTransitionPars[1] = -999;
295 fConvolutedGaussTransitionPars[2] = -999;
296
297 // Fraction histos
298 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
299 fFractionHists[i] = 0x0;
300 fFractionSysErrorHists[i] = 0x0;
9e95a906 301
302 fPtResolution[i] = 0x0;
e131b05f 303 }
304
305 // Define input and output slots here
306 // Input slot #0 works with a TChain
307 DefineInput(0, TChain::Class());
9e95a906 308
e131b05f 309 DefineOutput(1, TObjArray::Class());
310
311 DefineOutput(2, AliCFContainer::Class());
9e95a906 312
313 DefineOutput(3, TObjArray::Class());
e131b05f 314}
315
316
317//________________________________________________________________________
318AliAnalysisTaskPID::~AliAnalysisTaskPID()
319{
320 // dtor
321
322 CleanupParticleFractionHistos();
323
324 delete fOutputContainer;
9e95a906 325 fOutputContainer = 0x0;
326
327 delete fPtResolutionContainer;
328 fPtResolutionContainer = 0x0;
e131b05f 329
330 delete fConvolutedGausDeltaPrime;
9e95a906 331 fConvolutedGausDeltaPrime = 0x0;
e131b05f 332
333 delete [] fGenRespElDeltaPrimeEl;
334 delete [] fGenRespElDeltaPrimeKa;
335 delete [] fGenRespElDeltaPrimePi;
336 delete [] fGenRespElDeltaPrimePr;
337
338 fGenRespElDeltaPrimeEl = 0x0;
339 fGenRespElDeltaPrimeKa = 0x0;
340 fGenRespElDeltaPrimePi = 0x0;
341 fGenRespElDeltaPrimePr = 0x0;
342
343 delete [] fGenRespKaDeltaPrimeEl;
344 delete [] fGenRespKaDeltaPrimeKa;
345 delete [] fGenRespKaDeltaPrimePi;
346 delete [] fGenRespKaDeltaPrimePr;
347
348 fGenRespKaDeltaPrimeEl = 0x0;
349 fGenRespKaDeltaPrimeKa = 0x0;
350 fGenRespKaDeltaPrimePi = 0x0;
351 fGenRespKaDeltaPrimePr = 0x0;
352
353 delete [] fGenRespPiDeltaPrimeEl;
354 delete [] fGenRespPiDeltaPrimeKa;
355 delete [] fGenRespPiDeltaPrimePi;
356 delete [] fGenRespPiDeltaPrimePr;
357
358 fGenRespPiDeltaPrimeEl = 0x0;
359 fGenRespPiDeltaPrimeKa = 0x0;
360 fGenRespPiDeltaPrimePi = 0x0;
361 fGenRespPiDeltaPrimePr = 0x0;
362
363 delete [] fGenRespMuDeltaPrimeEl;
364 delete [] fGenRespMuDeltaPrimeKa;
365 delete [] fGenRespMuDeltaPrimePi;
366 delete [] fGenRespMuDeltaPrimePr;
367
368 fGenRespMuDeltaPrimeEl = 0x0;
369 fGenRespMuDeltaPrimeKa = 0x0;
370 fGenRespMuDeltaPrimePi = 0x0;
371 fGenRespMuDeltaPrimePr = 0x0;
372
373 delete [] fGenRespPrDeltaPrimeEl;
374 delete [] fGenRespPrDeltaPrimeKa;
375 delete [] fGenRespPrDeltaPrimePi;
376 delete [] fGenRespPrDeltaPrimePr;
377
378 fGenRespPrDeltaPrimeEl = 0x0;
379 fGenRespPrDeltaPrimeKa = 0x0;
380 fGenRespPrDeltaPrimePi = 0x0;
381 fGenRespPrDeltaPrimePr = 0x0;
382
383 /*OLD with deltaSpecies
384 delete [] fGenRespElDeltaEl;
385 delete [] fGenRespElDeltaKa;
386 delete [] fGenRespElDeltaPi;
387 delete [] fGenRespElDeltaPr;
388
389 fGenRespElDeltaEl = 0x0;
390 fGenRespElDeltaKa = 0x0;
391 fGenRespElDeltaPi = 0x0;
392 fGenRespElDeltaPr = 0x0;
393
394 delete [] fGenRespKaDeltaEl;
395 delete [] fGenRespKaDeltaKa;
396 delete [] fGenRespKaDeltaPi;
397 delete [] fGenRespKaDeltaPr;
398
399 fGenRespKaDeltaEl = 0x0;
400 fGenRespKaDeltaKa = 0x0;
401 fGenRespKaDeltaPi = 0x0;
402 fGenRespKaDeltaPr = 0x0;
403
404 delete [] fGenRespPiDeltaEl;
405 delete [] fGenRespPiDeltaKa;
406 delete [] fGenRespPiDeltaPi;
407 delete [] fGenRespPiDeltaPr;
408
409 fGenRespPiDeltaEl = 0x0;
410 fGenRespPiDeltaKa = 0x0;
411 fGenRespPiDeltaPi = 0x0;
412 fGenRespPiDeltaPr = 0x0;
413
414 delete [] fGenRespMuDeltaEl;
415 delete [] fGenRespMuDeltaKa;
416 delete [] fGenRespMuDeltaPi;
417 delete [] fGenRespMuDeltaPr;
418
419 fGenRespMuDeltaEl = 0x0;
420 fGenRespMuDeltaKa = 0x0;
421 fGenRespMuDeltaPi = 0x0;
422 fGenRespMuDeltaPr = 0x0;
423
424 delete [] fGenRespPrDeltaEl;
425 delete [] fGenRespPrDeltaKa;
426 delete [] fGenRespPrDeltaPi;
427 delete [] fGenRespPrDeltaPr;
428
429 fGenRespPrDeltaEl = 0x0;
430 fGenRespPrDeltaKa = 0x0;
431 fGenRespPrDeltaPi = 0x0;
432 fGenRespPrDeltaPr = 0x0;
433 */
434}
435
436
437//________________________________________________________________________
438void AliAnalysisTaskPID::SetUpPIDcombined()
439{
440 // Initialise the PIDcombined object
441
9e95a906 442 if (!fDoPID)
443 return;
444
445 if(fDebug > 1)
446 printf("File: %s, Line: %d: SetUpPIDcombined\n", (char*)__FILE__, __LINE__);
447
e131b05f 448 if (!fPIDcombined) {
449 AliFatal("No PIDcombined object!\n");
450 return;
451 }
452
453 fPIDcombined->SetSelectedSpecies(AliPID::kSPECIESC);
454 fPIDcombined->SetEnablePriors(fUsePriors);
455
456 if (fTPCDefaultPriors)
457 fPIDcombined->SetDefaultTPCPriors();
458
459 //TODO use individual priors...
460
461 // Change detector mask (TPC,TOF,ITS)
462 Int_t detectorMask = AliPIDResponse::kDetTPC;
463
464 // Reject mismatch mask - mismatch only relevant for TOF at the moment - other detectors do not use it
465 Int_t rejectMismatchMask = AliPIDResponse::kDetTPC;
466
467
468 if (fUseITS) {
469 detectorMask = detectorMask | AliPIDResponse::kDetITS;
470 rejectMismatchMask = rejectMismatchMask | AliPIDResponse::kDetITS;
471 }
472 if (fUseTOF) {
473 detectorMask = detectorMask | AliPIDResponse::kDetTOF;
474 rejectMismatchMask = rejectMismatchMask | AliPIDResponse::kDetTOF;
475 }
476
477 fPIDcombined->SetDetectorMask(detectorMask);
478 fPIDcombined->SetRejectMismatchMask(rejectMismatchMask);
9e95a906 479
480 if(fDebug > 1)
481 printf("File: %s, Line: %d: SetUpPIDcombined done\n", (char*)__FILE__, __LINE__);
e131b05f 482}
483
484
485//________________________________________________________________________
486void AliAnalysisTaskPID::UserCreateOutputObjects()
487{
488 // Create histograms
489 // Called once
490
9e95a906 491 if(fDebug > 1)
492 printf("File: %s, Line: %d: UserCreateOutputObjects\n", (char*)__FILE__, __LINE__);
493
e131b05f 494 SetUpPIDcombined();
495
496 // Input handler
497 AliAnalysisManager* man = AliAnalysisManager::GetAnalysisManager();
498 AliInputEventHandler* inputHandler = dynamic_cast<AliInputEventHandler*>(man->GetInputEventHandler());
499
500 if (!inputHandler)
501 AliFatal("Input handler needed");
502 else {
503 // PID response object
504 fPIDResponse = inputHandler->GetPIDResponse();
505 if (!fPIDResponse)
506 AliFatal("PIDResponse object was not created");
507 }
508
9e95a906 509 if(fDebug > 2)
510 printf("File: %s, Line: %d: UserCreateOutputObjects -> Retrieved PIDresponse object\n", (char*)__FILE__, __LINE__);
511
e131b05f 512 OpenFile(1);
9e95a906 513
514 if(fDebug > 2)
515 printf("File: %s, Line: %d: UserCreateOutputObjects -> OpenFile(1) successful\n", (char*)__FILE__, __LINE__);
516
e131b05f 517 fOutputContainer = new TObjArray(1);
9e95a906 518 fOutputContainer->SetName(GetName());
e131b05f 519 fOutputContainer->SetOwner(kTRUE);
520
521 const Int_t nPtBins = 68;
522 Double_t binsPt[nPtBins+1] = {0. , 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,
523 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95,
524 1.0, 1.1 , 1.2, 1.3 , 1.4, 1.5 , 1.6, 1.7 , 1.8, 1.9 ,
525 2.0, 2.2 , 2.4, 2.6 , 2.8, 3.0 , 3.2, 3.4 , 3.6, 3.8 ,
526 4.0, 4.5 , 5.0, 5.5 , 6.0, 6.5 , 7.0, 8.0 , 9.0, 10.0,
527 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0, 20.0, 22.0, 24.0,
528 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 40.0, 45.0, 50.0 };
529
530 const Int_t nCentBins = 12;
531 //-1 for pp; 90-100 has huge electro-magnetic impurities
532 Double_t binsCent[nCentBins+1] = {-1, 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 };
533
534 const Int_t nJetPtBins = 11;
535 Double_t binsJetPt[nJetPtBins+1] = {0, 2, 5, 10, 15, 20, 30, 40, 60, 80, 120, 200};
536
537 const Int_t nChargeBins = 2;
538 const Double_t binsCharge[nChargeBins+1] = { -1.0 - 1e-4, 0.0, 1.0 + 1e-4 };
539
540 const Int_t nBinsJets = kDataNumAxes;
541 const Int_t nBinsNoJets = nBinsJets - fgkNumJetAxes;
542
543 const Int_t nBins = fStoreAdditionalJetInformation ? nBinsJets : nBinsNoJets;
544
545 // deltaPrimeSpecies binning
546 const Int_t deltaPrimeNBins = 600;
547 Double_t deltaPrimeBins[deltaPrimeNBins + 1];
548
549 const Double_t fromLow = fkDeltaPrimeLowLimit;
550 const Double_t toHigh = fkDeltaPrimeUpLimit;
551 const Double_t factor = TMath::Power(toHigh/fromLow, 1./deltaPrimeNBins);
552
553 // Log binning for whole deltaPrime range
554 deltaPrimeBins[0] = fromLow;
555 for (Int_t i = 0 + 1; i <= deltaPrimeNBins; i++) {
556 deltaPrimeBins[i] = factor * deltaPrimeBins[i - 1];
557 }
558
559 const Int_t nMCPIDbins = 5;
560 const Double_t mcPIDmin = 0.;
561 const Double_t mcPIDmax = 5.;
562
563 const Int_t nSelSpeciesBins = 4;
564 const Double_t selSpeciesMin = 0.;
565 const Double_t selSpeciesMax = 4.;
566
567 const Int_t nZBins = 20;
568 const Double_t zMin = 0.;
569 const Double_t zMax = 1.;
570
571 const Int_t nXiBins = 70;
572 const Double_t xiMin = 0.;
573 const Double_t xiMax = 7.;
574
77324970
CKB		 575 const Int_t nTOFpidInfoBins = kNumTOFpidInfoBins;
576 const Double_t tofPIDinfoMin = kNoTOFinfo;
577 const Double_t tofPIDinfoMax = kNoTOFinfo + kNumTOFpidInfoBins;
578
e131b05f 579 // MC PID, SelectSpecies, pT, deltaPrimeSpecies, centrality percentile, jet pT, z = track_pT/jet_pT, xi = log(1/z)
77324970
CKB		 580 Int_t binsNoJets[nBinsNoJets] = { nMCPIDbins,
581 nSelSpeciesBins,
582 nPtBins,
583 deltaPrimeNBins,
584 nCentBins,
585 nChargeBins,
586 nTOFpidInfoBins };
587
588 Int_t binsJets[nBinsJets] = { nMCPIDbins,
589 nSelSpeciesBins,
590 nPtBins,
591 deltaPrimeNBins,
592 nCentBins,
593 nJetPtBins,
594 nZBins,
595 nXiBins,
596 nChargeBins,
597 nTOFpidInfoBins };
598
e131b05f 599 Int_t *bins = fStoreAdditionalJetInformation ? &binsJets[0] : &binsNoJets[0];
600
77324970
CKB		 601 Double_t xminNoJets[nBinsNoJets] = { mcPIDmin,
602 selSpeciesMin,
603 binsPt[0],
604 deltaPrimeBins[0],
605 binsCent[0],
606 binsCharge[0],
607 tofPIDinfoMin };
608
609 Double_t xminJets[nBinsJets] = { mcPIDmin,
610 selSpeciesMin,
611 binsPt[0],
612 deltaPrimeBins[0],
613 binsCent[0],
614 binsJetPt[0],
615 zMin,
616 xiMin,
617 binsCharge[0],
618 tofPIDinfoMin };
619
e131b05f 620 Double_t *xmin = fStoreAdditionalJetInformation? &xminJets[0] : &xminNoJets[0];
621
77324970
CKB		 622 Double_t xmaxNoJets[nBinsNoJets] = { mcPIDmax,
623 selSpeciesMax,
624 binsPt[nPtBins],
625 deltaPrimeBins[deltaPrimeNBins],
626 binsCent[nCentBins],
627 binsCharge[nChargeBins],
628 tofPIDinfoMax };
629
630 Double_t xmaxJets[nBinsJets] = { mcPIDmax,
631 selSpeciesMax,
632 binsPt[nPtBins],
633 deltaPrimeBins[deltaPrimeNBins],
634 binsCent[nCentBins],
635 binsJetPt[nJetPtBins],
636 zMax,
637 xiMax,
638 binsCharge[nChargeBins],
639 tofPIDinfoMax };
e131b05f 640
77324970 641 Double_t *xmax = fStoreAdditionalJetInformation? &xmaxJets[0] : &xmaxNoJets[0];
e131b05f 642
643 fConvolutedGausDeltaPrime->SetNpx(deltaPrimeNBins);
644
9e95a906 645 if (fDoPID) {
646 fhPIDdataAll = new THnSparseD("hPIDdataAll","", nBins, bins, xmin, xmax);
647 SetUpHist(fhPIDdataAll, binsPt, deltaPrimeBins, binsCent, binsJetPt);
648 fOutputContainer->Add(fhPIDdataAll);
649 }
e131b05f 650
651 // Generated histograms (so far, bins are the same as for primary THnSparse)
652 const Int_t nGenBins = fStoreAdditionalJetInformation ? nBinsJets : nBinsNoJets;
653 // MC PID, SelectSpecies, Pt, deltaPrimeSpecies, jet pT, z = track_pT/jet_pT, xi = log(1/z)
654
655 Int_t *genBins = fStoreAdditionalJetInformation ? &binsJets[0] : &binsNoJets[0];
656 Double_t *genXmin = fStoreAdditionalJetInformation? &xminJets[0] : &xminNoJets[0];
657 Double_t *genXmax = fStoreAdditionalJetInformation? &xmaxJets[0] : &xmaxNoJets[0];
658
9e95a906 659 if (fDoPID) {
660 fhGenEl = new THnSparseD("hGenEl", "", nGenBins, genBins, genXmin, genXmax);
661 SetUpGenHist(fhGenEl, binsPt, deltaPrimeBins, binsCent, binsJetPt);
662 fOutputContainer->Add(fhGenEl);
663
664 fhGenKa = new THnSparseD("hGenKa", "", nGenBins, genBins, genXmin, genXmax);
665 SetUpGenHist(fhGenKa, binsPt, deltaPrimeBins, binsCent, binsJetPt);
666 fOutputContainer->Add(fhGenKa);
667
668 fhGenPi = new THnSparseD("hGenPi", "", nGenBins, genBins, genXmin, genXmax);
669 SetUpGenHist(fhGenPi, binsPt, deltaPrimeBins, binsCent, binsJetPt);
670 fOutputContainer->Add(fhGenPi);
671
672 if (fTakeIntoAccountMuons) {
673 fhGenMu = new THnSparseD("hGenMu", "", nGenBins, genBins, genXmin, genXmax);
674 SetUpGenHist(fhGenMu, binsPt, deltaPrimeBins, binsCent, binsJetPt);
675 fOutputContainer->Add(fhGenMu);
676 }
677
678 fhGenPr = new THnSparseD("hGenPr", "", nGenBins, genBins, genXmin, genXmax);
679 SetUpGenHist(fhGenPr, binsPt, deltaPrimeBins, binsCent, binsJetPt);
680 fOutputContainer->Add(fhGenPr);
681
9e95a906 682 fhSkippedTracksForSignalGeneration = new TH2D("fhSkippedTracksForSignalGeneration",
5709c40a 683 "Number of tracks skipped for the signal generation;p_{T}^{gen} (GeV/c);TPC signal N",
9e95a906 684 nPtBins, binsPt, 161, -0.5, 160.5);
685 fhSkippedTracksForSignalGeneration->Sumw2();
686 fOutputContainer->Add(fhSkippedTracksForSignalGeneration);
e131b05f 687 }
688
e131b05f 689
4b4d71d4 690 fhEventsProcessed = new TH1D("fhEventsProcessed",
691 "Number of events passing trigger selection, vtx and zvtx cuts;Centrality percentile",
692 nCentBins, binsCent);
693 fhEventsProcessed->Sumw2();
694 fOutputContainer->Add(fhEventsProcessed);
695
696 fhEventsTriggerSelVtxCut = new TH1D("fhEventsTriggerSelVtxCut",
697 "Number of events passing trigger selection and vtx cut;Centrality percentile",
698 nCentBins, binsCent);
699 fhEventsTriggerSelVtxCut->Sumw2();
700 fOutputContainer->Add(fhEventsTriggerSelVtxCut);
701
702 fhEventsTriggerSel = new TH1D("fhEventsTriggerSel",
703 "Number of events passing trigger selection;Centrality percentile",
704 nCentBins, binsCent);
705 fOutputContainer->Add(fhEventsTriggerSel);
706 fhEventsTriggerSel->Sumw2();
707
708
e131b05f 709 // Generated yields within acceptance
710 const Int_t nBinsGenYields = fStoreAdditionalJetInformation ? kGenYieldNumAxes : kGenYieldNumAxes - 3;
711 Int_t genYieldsBins[kGenYieldNumAxes] = { nMCPIDbins, nPtBins, nCentBins, nJetPtBins, nZBins, nXiBins,
712 nChargeBins };
713 genYieldsBins[GetIndexOfChargeAxisGenYield()] = nChargeBins;
714 Double_t genYieldsXmin[kGenYieldNumAxes] = { mcPIDmin, binsPt[0], binsCent[0], binsJetPt[0], zMin, xiMin,
715 binsCharge[0] };
716 genYieldsXmin[GetIndexOfChargeAxisGenYield()] = binsCharge[0];
717 Double_t genYieldsXmax[kGenYieldNumAxes] = { mcPIDmax, binsPt[nPtBins], binsCent[nCentBins], binsJetPt[nJetPtBins], zMax, xiMax,
718 binsCharge[nChargeBins] };
719 genYieldsXmax[GetIndexOfChargeAxisGenYield()] = binsCharge[nChargeBins];
720
9e95a906 721 if (fDoPID) {
722 fhMCgeneratedYieldsPrimaries = new THnSparseD("fhMCgeneratedYieldsPrimaries",
723 "Generated yields w/o reco and cuts inside acceptance (physical primaries)",
724 nBinsGenYields, genYieldsBins, genYieldsXmin, genYieldsXmax);
725 SetUpGenYieldHist(fhMCgeneratedYieldsPrimaries, binsPt, binsCent, binsJetPt);
726 fOutputContainer->Add(fhMCgeneratedYieldsPrimaries);
e131b05f 727 }
728
9e95a906 729 // Container with several process steps (generated and reconstructed level with some variations)
730 if (fDoEfficiency) {
731 OpenFile(2);
732
733 if(fDebug > 2)
734 printf("File: %s, Line: %d: UserCreateOutputObjects -> OpenFile(2) successful\n", (char*)__FILE__, __LINE__);
e131b05f 735
9e95a906 736 // Array for the number of bins in each dimension
737 // Dimensions: MC-ID, trackPt, trackEta, trackCharge, cenrality percentile, jetPt, z, xi TODO phi???
738 const Int_t nEffDims = fStoreAdditionalJetInformation ? kEffNumAxes : kEffNumAxes - 3; // Number of dimensions for the efficiency
739
740 const Int_t nMCIDbins = AliPID::kSPECIES;
9d7ad2e4 741 Double_t binsMCID[nMCIDbins + 1];
9e95a906 742
9d7ad2e4 743 for(Int_t i = 0; i <= nMCIDbins; i++) {
9e95a906 744 binsMCID[i]= i;
745 }
746
747 const Int_t nEtaBins = 18;
748 const Double_t binsEta[nEtaBins+1] = {-0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1,
749 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 };
750
751 const Int_t nEffBins[kEffNumAxes] = { nMCIDbins, nPtBins, nEtaBins, nChargeBins, nCentBins, nJetPtBins, nZBins, nXiBins };
752
753 fContainerEff = new AliCFContainer("containerEff", "Reconstruction Efficiency x Acceptance x Resolution and Secondary Correction",
754 kNumSteps, nEffDims, nEffBins);
755
756 // Setting the bin limits
757 fContainerEff->SetBinLimits(kEffMCID, binsMCID);
758 fContainerEff->SetBinLimits(kEffTrackPt, binsPt);
759 fContainerEff->SetBinLimits(kEffTrackEta, binsEta);
760 fContainerEff->SetBinLimits(kEffTrackCharge, binsCharge);
761 fContainerEff->SetBinLimits(kEffCentrality, binsCent);
762 if (fStoreAdditionalJetInformation) {
763 fContainerEff->SetBinLimits(kEffJetPt, binsJetPt);
764 fContainerEff->SetBinLimits(kEffZ, zMin, zMax);
765 fContainerEff->SetBinLimits(kEffXi, xiMin, xiMax);
766 }
767
768 fContainerEff->SetVarTitle(kEffMCID,"MC ID");
5709c40a 769 fContainerEff->SetVarTitle(kEffTrackPt,"p_{T} (GeV/c)");
9e95a906 770 fContainerEff->SetVarTitle(kEffTrackEta,"#eta");
771 fContainerEff->SetVarTitle(kEffTrackCharge,"Charge (e_{0})");
772 fContainerEff->SetVarTitle(kEffCentrality, "Centrality Percentile");
773 if (fStoreAdditionalJetInformation) {
5709c40a 774 fContainerEff->SetVarTitle(kEffJetPt, "p_{T}^{jet} (GeV/c)");
775 fContainerEff->SetVarTitle(kEffZ, "z = p_{T}^{track} / p_{T}^{jet}");
776 fContainerEff->SetVarTitle(kEffXi, "#xi = ln(p_{T}^{jet} / p_{T}^{track})");
9e95a906 777 }
778
779 // Define clean MC sample
780 fContainerEff->SetStepTitle(kStepGenWithGenCuts, "Particle level, cuts on particle level");
781 // For Acceptance x Efficiency correction of primaries
782 fContainerEff->SetStepTitle(kStepRecWithGenCuts, "Detector level (rec) with cuts on particle level");
783 // For (pT) resolution correction
784 fContainerEff->SetStepTitle(kStepRecWithGenCutsMeasuredObs,
785 "Detector level (rec) with cuts on particle level with measured observables");
786 // For secondary correction
787 fContainerEff->SetStepTitle(kStepRecWithRecCutsMeasuredObs,
788 "Detector level, all cuts on detector level with measured observables");
789 fContainerEff->SetStepTitle(kStepRecWithRecCutsPrimaries,
790 "Detector level, all cuts on detector level, only MC primaries");
791 fContainerEff->SetStepTitle(kStepRecWithRecCutsMeasuredObsPrimaries,
792 "Detector level, all cuts on detector level with measured observables, only MC primaries");
793 fContainerEff->SetStepTitle(kStepRecWithRecCutsMeasuredObsStrangenessScaled,
794 "Detector level (strangeness scaled), all cuts on detector level with measured observables");
795 }
796
797 if (fDoPID || fDoEfficiency) {
798 // Generated jets
5709c40a 799 fh2FFJetPtRec = new TH2D("fh2FFJetPtRec", "Number of reconstructed jets;Centrality Percentile;p_{T}^{jet} (GeV/c)",
9e95a906 800 nCentBins, binsCent, nJetPtBins, binsJetPt);
801 fh2FFJetPtRec->Sumw2();
802 fOutputContainer->Add(fh2FFJetPtRec);
5709c40a 803 fh2FFJetPtGen = new TH2D("fh2FFJetPtGen", "Number of generated jets;Centrality Percentile;p_{T}^{jet} (GeV/c)",
9e95a906 804 nCentBins, binsCent, nJetPtBins, binsJetPt);
805 fh2FFJetPtGen->Sumw2();
806 fOutputContainer->Add(fh2FFJetPtGen);
e131b05f 807 }
808
e131b05f 809 // Pythia information
810 fh1Xsec = new TProfile("fh1Xsec", "xsec from pyxsec.root", 1, 0, 1);
811 fh1Xsec->Sumw2();
812 fh1Xsec->GetXaxis()->SetBinLabel(1, "<#sigma>");
813 fh1Trials = new TH1D("fh1Trials", "trials from pyxsec.root", 1, 0, 1);
814 fh1Trials->Sumw2();
815 fh1Trials->GetXaxis()->SetBinLabel(1, "#sum{ntrials}");
816
817 fOutputContainer->Add(fh1Xsec);
818 fOutputContainer->Add(fh1Trials);
819
9e95a906 820 if (fDoPtResolution) {
821 OpenFile(3);
822
823 if(fDebug > 2)
824 printf("File: %s, Line: %d: UserCreateOutputObjects -> OpenFile(3) successful\n", (char*)__FILE__, __LINE__);
825
826 fPtResolutionContainer = new TObjArray(1);
827 fPtResolutionContainer->SetName(Form("%s_PtResolution", GetName()));
828 fPtResolutionContainer->SetOwner(kTRUE);
829
e4351829 830 const Int_t nPtBinsRes = 100;
831 Double_t pTbinsRes[nPtBinsRes + 1];
832
833 const Double_t fromLowPtRes = 0.15;
834 const Double_t toHighPtRes = 50.;
835 const Double_t factorPtRes = TMath::Power(toHighPtRes/fromLowPtRes, 1./nPtBinsRes);
836 // Log binning for whole pT range
837 pTbinsRes[0] = fromLowPtRes;
838 for (Int_t i = 0 + 1; i <= nPtBinsRes; i++) {
839 pTbinsRes[i] = factorPtRes * pTbinsRes[i - 1];
840 }
841
9e95a906 842 const Int_t nBinsPtResolution = kPtResNumAxes;
e4351829 843 Int_t ptResolutionBins[kPtResNumAxes] = { nJetPtBins, nPtBinsRes, nPtBinsRes,
844 nChargeBins, nCentBins };
845 Double_t ptResolutionXmin[kPtResNumAxes] = { binsJetPt[0], pTbinsRes[0], pTbinsRes[0],
846 binsCharge[0], binsCent[0] };
847 Double_t ptResolutionXmax[kPtResNumAxes] = { binsJetPt[nJetPtBins], pTbinsRes[nPtBinsRes], pTbinsRes[nPtBinsRes],
848 binsCharge[nChargeBins], binsCent[nCentBins] };
9e95a906 849
850 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
851 fPtResolution[i] = new THnSparseD(Form("fPtResolution_%s", AliPID::ParticleShortName(i)),
852 Form("Pt resolution for primaries, %s", AliPID::ParticleLatexName(i)),
853 nBinsPtResolution, ptResolutionBins, ptResolutionXmin, ptResolutionXmax);
e4351829 854 SetUpPtResHist(fPtResolution[i], pTbinsRes, binsJetPt, binsCent);
9e95a906 855 fPtResolutionContainer->Add(fPtResolution[i]);
856 }
857 }
858
859 if(fDebug > 2)
860 printf("File: %s, Line: %d: UserCreateOutputObjects -> Posting output data\n", (char*)__FILE__, __LINE__);
861
77324970
CKB		 862 PostData(1, fOutputContainer);
863 PostData(2, fContainerEff);
864 PostData(3, fPtResolutionContainer);
9e95a906 865
866 if(fDebug > 2)
867 printf("File: %s, Line: %d: UserCreateOutputObjects -> Done\n", (char*)__FILE__, __LINE__);
e131b05f 868}
869
870
871//________________________________________________________________________
872void AliAnalysisTaskPID::UserExec(Option_t *)
873{
874 // Main loop
875 // Called for each event
9d7ad2e4 876
9e95a906 877 if(fDebug > 1)
878 printf("File: %s, Line: %d: UserExec\n", (char*)__FILE__, __LINE__);
879
e131b05f 880 // No processing of event, if input is fed in directly from another task
881 if (fInputFromOtherTask)
882 return;
9e95a906 883
884 if(fDebug > 1)
885 printf("File: %s, Line: %d: UserExec -> Processing started\n", (char*)__FILE__, __LINE__);
e131b05f 886
887 fEvent = dynamic_cast<AliVEvent*>(InputEvent());
888 if (!fEvent) {
889 Printf("ERROR: fEvent not available");
890 return;
891 }
892
893 fMC = dynamic_cast<AliMCEvent*>(MCEvent());
894
895 if (!fPIDResponse || !fPIDcombined)
896 return;
897
4b4d71d4 898 Double_t centralityPercentile = -1;
899 if (fStoreCentralityPercentile)
900 centralityPercentile = fEvent->GetCentrality()->GetCentralityPercentile(fCentralityEstimator.Data());
901
902 IncrementEventCounter(centralityPercentile, kTriggerSel);
903
904 // Check if vertex is ok, but don't apply cut on z position
905 if (!GetVertexIsOk(fEvent, kFALSE))
e131b05f 906 return;
907
908 fESD = dynamic_cast<AliESDEvent*>(fEvent);
909 const AliVVertex* primaryVertex = fESD ? fESD->GetPrimaryVertexTracks() : fEvent->GetPrimaryVertex();
910 if (!primaryVertex)
911 return;
912
913 if(primaryVertex->GetNContributors() <= 0)
914 return;
915
4b4d71d4 916 IncrementEventCounter(centralityPercentile, kTriggerSelAndVtxCut);
e131b05f 917
4b4d71d4 918 // Now check again, but also require z position to be in desired range
919 if (!GetVertexIsOk(fEvent, kTRUE))
920 return;
e131b05f 921
4b4d71d4 922 IncrementEventCounter(centralityPercentile, kTriggerSelAndVtxCutAndZvtxCut);
923
924 Double_t magField = fEvent->GetMagneticField();
e131b05f 925
926 if (fMC) {
9e95a906 927 if (fDoPID || fDoEfficiency) {
928 for (Int_t iPart = 0; iPart < fMC->GetNumberOfTracks(); iPart++) {
929 AliMCParticle *mcPart = dynamic_cast<AliMCParticle*>(fMC->GetTrack(iPart));
930
931 if (!mcPart)
932 continue;
933
934 // Define clean MC sample with corresponding particle level track cuts:
935 // - MC-track must be in desired eta range
936 // - MC-track must be physical primary
937 // - Species must be one of those in question (everything else goes to the overflow bin of mcID)
938
939 // Geometrie should be the same as on the reconstructed level -> By definition analysis within this eta interval
77324970 940 if (!IsInAcceptedEtaRange(TMath::Abs(mcPart->Eta()))) continue;
9e95a906 941
942 Int_t mcID = PDGtoMCID(mcPart->PdgCode());
943
944 // AliMCParticle->Charge() calls TParticlePDG->Charge(), which returns the charge in units of e0 / 3
945 Double_t chargeMC = mcPart->Charge() / 3.;
946
947 if (TMath::Abs(chargeMC) < 0.01)
948 continue; // Reject neutral particles (only relevant, if mcID is not used)
949
950 if (!fMC->IsPhysicalPrimary(iPart))
951 continue;
952
953 if (fDoPID) {
954 Double_t valuesGenYield[kGenYieldNumAxes] = { mcID, mcPart->Pt(), centralityPercentile, -1, -1, -1, -1 };
955 valuesGenYield[GetIndexOfChargeAxisGenYield()] = chargeMC;
956
957 fhMCgeneratedYieldsPrimaries->Fill(valuesGenYield);
958 }
959
960
961 if (fDoEfficiency) {
962 Double_t valueEff[kEffNumAxes] = { mcID, mcPart->Pt(), mcPart->Eta(), chargeMC, centralityPercentile,
963 -1, -1, -1 };
964
965 fContainerEff->Fill(valueEff, kStepGenWithGenCuts);
966 }
967 }
e131b05f 968 }
969 }
970
971 // Track loop to fill a Train spectrum
972 for (Int_t iTracks = 0; iTracks < fEvent->GetNumberOfTracks(); iTracks++) {
973 AliVTrack* track = dynamic_cast<AliVTrack*>(fEvent->GetTrack(iTracks));
974 if (!track) {
975 Printf("ERROR: Could not retrieve track %d", iTracks);
976 continue;
977 }
978
979
980 // Apply detector level track cuts
a6852ea8 981 Double_t dEdxTPC = fPIDResponse->IsTunedOnData() ? fPIDResponse->GetTPCsignalTunedOnData(track) : track->GetTPCsignal();
982 if (dEdxTPC <= 0)
983 continue;
e131b05f 984
985 if(fTrackFilter && !fTrackFilter->IsSelected(track))
986 continue;
987
493982d9 988 if (GetUseTPCCutMIGeo()) {
a6852ea8 989 if (!TPCCutMIGeo(track, fEvent))
990 continue;
991 }
493982d9
ML		 992 else if (GetUseTPCnclCut()) {
993 if (!TPCnclCut(track))
994 continue;
995 }
e131b05f 996
997 if(fUsePhiCut) {
998 if (!PhiPrimeCut(track, magField))
999 continue; // reject track
1000 }
1001
e131b05f 1002 Int_t pdg = 0; // = 0 indicates data for the moment
1003 AliMCParticle* mcTrack = 0x0;
1004 Int_t mcID = AliPID::kUnknown;
1005 Int_t label = 0;
1006
1007 if (fMC) {
1008 label = track->GetLabel();
1009
1010 //if (label < 0)
1011 // continue;
1012
1013 mcTrack = dynamic_cast<AliMCParticle*>(fMC->GetTrack(TMath::Abs(label)));
1014 if (!mcTrack) {
1015 Printf("ERROR: Could not retrieve mcTrack with label %d for track %d", label, iTracks);
1016 continue;
1017 }
1018
1019 pdg = mcTrack->PdgCode();
1020 mcID = PDGtoMCID(mcTrack->PdgCode());
1021
9e95a906 1022 if (fDoEfficiency) {
1023 // For efficiency: Reconstructed track has survived all cuts on the detector level (excluding acceptance)
1024 // and has an associated MC track which is a physical primary and was generated inside the acceptance
1025 if (fMC->IsPhysicalPrimary(TMath::Abs(label)) &&
77324970 1026 IsInAcceptedEtaRange(TMath::Abs(mcTrack->Eta()))) {
e131b05f 1027
9e95a906 1028 // AliMCParticle->Charge() calls TParticlePDG->Charge(), which returns the charge in units of e0 / 3
1029 Double_t value[kEffNumAxes] = { mcID, mcTrack->Pt(), mcTrack->Eta(), mcTrack->Charge() / 3., centralityPercentile,
1030 -1, -1, -1 };
1031 fContainerEff->Fill(value, kStepRecWithGenCuts);
1032
1033 Double_t valueMeas[kEffNumAxes] = { mcID, track->Pt(), track->Eta(), track->Charge(), centralityPercentile,
1034 -1, -1, -1 };
1035 fContainerEff->Fill(valueMeas, kStepRecWithGenCutsMeasuredObs);
1036 }
e131b05f 1037 }
1038 }
1039
1040 // Only process tracks inside the desired eta window
77324970 1041 if (!IsInAcceptedEtaRange(TMath::Abs(track->Eta()))) continue;
e131b05f 1042
9e95a906 1043 if (fDoPID)
1044 ProcessTrack(track, pdg, centralityPercentile, -1); // No jet information in this case -> Set jet pT to -1
e131b05f 1045
9e95a906 1046 if (fDoPtResolution) {
1047 if (mcTrack && fMC->IsPhysicalPrimary(TMath::Abs(label))) {
e4351829 1048 // AliMCParticle->Charge() calls TParticlePDG->Charge(), which returns the charge in units of e0 / 3
1049 Double_t valuePtRes[kPtResNumAxes] = { -1, mcTrack->Pt(), track->Pt(), mcTrack->Charge() / 3., centralityPercentile };
44ed76dd 1050 FillPtResolution(mcID, valuePtRes);
9e95a906 1051 }
1052 }
1053
1054 if (fDoEfficiency) {
1055 if (mcTrack) {
1056 Double_t valueRecAllCuts[kEffNumAxes] = { mcID, track->Pt(), track->Eta(), track->Charge(), centralityPercentile,
1057 -1, -1, -1 };
1058 fContainerEff->Fill(valueRecAllCuts, kStepRecWithRecCutsMeasuredObs);
1059
1060 Double_t weight = IsSecondaryWithStrangeMotherMC(fMC, TMath::Abs(label)) ?
1061 GetMCStrangenessFactorCMS(fMC, mcTrack) : 1.0;
1062 fContainerEff->Fill(valueRecAllCuts, kStepRecWithRecCutsMeasuredObsStrangenessScaled, weight);
1063
1064 // AliMCParticle->Charge() calls TParticlePDG->Charge(), which returns the charge in units of e0 / 3
1065 Double_t valueGenAllCuts[kEffNumAxes] = { mcID, mcTrack->Pt(), mcTrack->Eta(), mcTrack->Charge() / 3.,
1066 centralityPercentile, -1, -1, -1 };
1067 if (fMC->IsPhysicalPrimary(TMath::Abs(label))) {
1068 fContainerEff->Fill(valueRecAllCuts, kStepRecWithRecCutsMeasuredObsPrimaries);
1069 fContainerEff->Fill(valueGenAllCuts, kStepRecWithRecCutsPrimaries);
1070 }
1071 }
e131b05f 1072 }
1073 } //track loop
1074
9e95a906 1075 if(fDebug > 2)
1076 printf("File: %s, Line: %d: UserExec -> Processing done\n", (char*)__FILE__, __LINE__);
1077
e131b05f 1078 PostOutputData();
9e95a906 1079
1080 if(fDebug > 2)
1081 printf("File: %s, Line: %d: UserExec -> Done\n", (char*)__FILE__, __LINE__);
e131b05f 1082}
1083
1084//________________________________________________________________________
1085void AliAnalysisTaskPID::Terminate(const Option_t *)
1086{
1087 // Draw result to the screen
1088 // Called once at the end of the query
1089}
1090
1091
1092//_____________________________________________________________________________
1093void AliAnalysisTaskPID::CheckDoAnyStematicStudiesOnTheExpectedSignal()
1094{
1095 // Check whether at least one scale factor indicates the ussage of systematic studies
1096 // and set internal flag accordingly.
1097
1098 fDoAnySystematicStudiesOnTheExpectedSignal = kFALSE;
1099
21b0adb1 1100
1101 if ((TMath::Abs(fSystematicScalingSplinesBelowThreshold - 1.0) > fgkEpsilon) ||
1102 (TMath::Abs(fSystematicScalingSplinesAboveThreshold - 1.0) > fgkEpsilon)) {
e131b05f 1103 fDoAnySystematicStudiesOnTheExpectedSignal = kTRUE;
1104 return;
1105 }
1106
1107 if ((TMath::Abs(fSystematicScalingEtaCorrectionLowMomenta - 1.0) > fgkEpsilon) ||
1108 (TMath::Abs(fSystematicScalingEtaCorrectionHighMomenta - 1.0) > fgkEpsilon)) {
1109 fDoAnySystematicStudiesOnTheExpectedSignal = kTRUE;
1110 return;
1111 }
1112
5709c40a 1113 if ((TMath::Abs(fSystematicScalingEtaSigmaParaBelowThreshold - 1.0) > fgkEpsilon) ||
1114 (TMath::Abs(fSystematicScalingEtaSigmaParaAboveThreshold - 1.0) > fgkEpsilon)) {
e131b05f 1115 fDoAnySystematicStudiesOnTheExpectedSignal = kTRUE;
1116 return;
1117 }
1118
1119 if (TMath::Abs(fSystematicScalingMultCorrection - 1.0) > fgkEpsilon) {
1120 fDoAnySystematicStudiesOnTheExpectedSignal = kTRUE;
1121 return;
1122 }
1123}
1124
1125
1126//_____________________________________________________________________________
1127Int_t AliAnalysisTaskPID::PDGtoMCID(Int_t pdg)
1128{
1129 // Returns the corresponding AliPID index to the given pdg code.
1130 // Returns AliPID::kUnkown if pdg belongs to a not considered species.
1131
1132 Int_t absPDGcode = TMath::Abs(pdg);
1133 if (absPDGcode == 211) {//Pion
1134 return AliPID::kPion;
1135 }
1136 else if (absPDGcode == 321) {//Kaon
1137 return AliPID::kKaon;
1138 }
1139 else if (absPDGcode == 2212) {//Proton
1140 return AliPID::kProton;
1141 }
1142 else if (absPDGcode == 11) {//Electron
1143 return AliPID::kElectron;
1144 }
1145 else if (absPDGcode == 13) {//Muon
1146 return AliPID::kMuon;
1147 }
1148
1149 return AliPID::kUnknown;
1150}
1151
1152
1153//_____________________________________________________________________________
9d7ad2e4 1154void AliAnalysisTaskPID::GetJetTrackObservables(Double_t trackPt, Double_t jetPt, Double_t& z, Double_t& xi)
e131b05f 1155{
1156 // Uses trackPt and jetPt to obtain z and xi.
1157
1158 z = (jetPt > 0 && trackPt >= 0) ? (trackPt / jetPt) : -1;
1159 xi = (z > 0) ? TMath::Log(1. / z) : -1;
1160
1161 if(trackPt > (1. - 1e-06) * jetPt && trackPt < (1. + 1e-06) * jetPt) { // case z=1 : move entry to last histo bin <1
1162 z = 1. - 1e-06;
1163 xi = 1e-06;
1164 }
1165}
1166
1167
1168//_____________________________________________________________________________
1169void AliAnalysisTaskPID::CleanupParticleFractionHistos()
1170{
1171 // Delete histos with particle fractions
1172
1173 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1174 delete fFractionHists[i];
1175 fFractionHists[i] = 0x0;
1176
1177 delete fFractionSysErrorHists[i];
1178 fFractionSysErrorHists[i] = 0x0;
1179 }
1180}
1181
1182
1183//_____________________________________________________________________________
1184Double_t AliAnalysisTaskPID::ConvolutedGaus(const Double_t* xx, const Double_t* par) const
1185{
1186 // Convolutes gauss with an exponential tail which describes dEdx-response better than pure gaussian
1187
1188 const Double_t mean = par[0];
1189 const Double_t sigma = par[1];
1190 const Double_t lambda = par[2];
1191
9e95a906 1192 if(fDebug > 5)
1193 printf("File: %s, Line: %d: ConvolutedGaus: mean %e, sigma %e, lambda %e\n", (char*)__FILE__, __LINE__, mean, sigma, lambda);
1194
e131b05f 1195 return lambda/sigma*TMath::Exp(-lambda/sigma*(xx[0]-mean)+lambda*lambda*0.5)*0.5*TMath::Erfc((-xx[0]+mean+sigma*lambda)/sigma*fgkOneOverSqrt2);
1196}
1197
1198
1199//_____________________________________________________________________________
9d7ad2e4 1200inline Double_t AliAnalysisTaskPID::FastGaus(Double_t x, Double_t mean, Double_t sigma) const
e131b05f 1201{
1202 // Calculate an unnormalised gaussian function with mean and sigma.
1203
1204 if (sigma < fgkEpsilon)
1205 return 1.e30;
1206
1207 const Double_t arg = (x - mean) / sigma;
1208 return exp(-0.5 * arg * arg);
1209}
1210
1211
1212//_____________________________________________________________________________
9d7ad2e4 1213inline Double_t AliAnalysisTaskPID::FastNormalisedGaus(Double_t x, Double_t mean, Double_t sigma) const
e131b05f 1214{
1215 // Calculate a normalised (divided by sqrt(2*Pi)*sigma) gaussian function with mean and sigma.
1216
1217 if (sigma < fgkEpsilon)
1218 return 1.e30;
1219
1220 const Double_t arg = (x - mean) / sigma;
1221 const Double_t res = exp(-0.5 * arg * arg);
1222 return res / (2.50662827463100024 * sigma); //sqrt(2*Pi)=2.50662827463100024
1223}
1224
1225
1226//_____________________________________________________________________________
1227Int_t AliAnalysisTaskPID::FindBinWithinRange(TAxis* axis, Double_t value) const
1228{
1229 // Find the corresponding bin of the axis. Values outside the range (also under and overflow) will be set to the first/last
1230 // available bin
1231
1232 Int_t bin = axis->FindFixBin(value);
1233
1234 if (bin <= 0)
1235 bin = 1;
1236 if (bin > axis->GetNbins())
1237 bin = axis->GetNbins();
1238
1239 return bin;
1240}
1241
1242
1243//_____________________________________________________________________________
9d7ad2e4 1244Int_t AliAnalysisTaskPID::FindFirstBinAboveIn3dSubset(const TH3* hist, Double_t threshold, Int_t yBin,
1245 Int_t zBin) const
e131b05f 1246{
1247 // Kind of projects a TH3 to 1 bin combination in y and z
1248 // and looks for the first x bin above a threshold for this projection.
1249 // If no such bin is found, -1 is returned.
1250
1251 if (!hist)
1252 return -1;
1253
1254 Int_t nBinsX = hist->GetNbinsX();
1255 for (Int_t xBin = 1; xBin <= nBinsX; xBin++) {
1256 if (hist->GetBinContent(xBin, yBin, zBin) > threshold)
1257 return xBin;
1258 }
1259
1260 return -1;
1261}
1262
1263
1264//_____________________________________________________________________________
9d7ad2e4 1265Int_t AliAnalysisTaskPID::FindLastBinAboveIn3dSubset(const TH3* hist, Double_t threshold, Int_t yBin,
1266 Int_t zBin) const
e131b05f 1267{
1268 // Kind of projects a TH3 to 1 bin combination in y and z
1269 // and looks for the last x bin above a threshold for this projection.
1270 // If no such bin is found, -1 is returned.
1271
1272 if (!hist)
1273 return -1;
1274
1275 Int_t nBinsX = hist->GetNbinsX();
1276 for (Int_t xBin = nBinsX; xBin >= 1; xBin--) {
1277 if (hist->GetBinContent(xBin, yBin, zBin) > threshold)
1278 return xBin;
1279 }
1280
1281 return -1;
1282}
1283
1284
1285//_____________________________________________________________________________
9d7ad2e4 1286Bool_t AliAnalysisTaskPID::GetParticleFraction(Double_t trackPt, Double_t jetPt, Double_t centralityPercentile,
1287 AliPID::EParticleType species,
e131b05f 1288 Double_t& fraction, Double_t& fractionErrorStat, Double_t& fractionErrorSys) const
1289{
1290 // Computes the particle fraction for the corresponding species for the given trackPt, jetPt and centrality.
1291 // Use jetPt = -1 for inclusive spectra and centralityPercentile = -1 for pp.
1292 // On success (return value kTRUE), fraction contains the particle fraction, fractionErrorStat(Sys) the sigma of its
1293 // statistical (systematic) error
1294
1295 fraction = -999.;
1296 fractionErrorStat = 999.;
1297 fractionErrorSys = 999.;
1298
1299 if (species > AliPID::kProton || species < AliPID::kElectron) {
1300 AliError(Form("Only fractions for species index %d to %d availabe, but not for the requested one: %d", 0, AliPID::kProton, species));
1301 return kFALSE;
1302 }
1303
1304 if (!fFractionHists[species]) {
1305 AliError(Form("Histo with particle fractions for species %d not loaded!", species));
1306
1307 return kFALSE;
1308 }
1309
1310 Int_t jetPtBin = FindBinWithinRange(fFractionHists[species]->GetYaxis(), jetPt);
1311 Int_t centBin = FindBinWithinRange(fFractionHists[species]->GetZaxis(), centralityPercentile);
1312
1313 // The following interpolation takes the bin content of the first/last available bin,
1314 // if requested point lies beyond bin center of first/last bin.
1315 // The interpolation is only done for the x-axis (track pT), i.e. jetPtBin and centBin are fix,
1316 // because the analysis will anyhow be run in bins of jetPt and centrality and
1317 // it is not desired to correlate different jetPt bins via interpolation.
1318
1319 // The same procedure is used for the error of the fraction
1320 TAxis* xAxis = fFractionHists[species]->GetXaxis();
1321
1322 // No interpolation to values beyond the centers of the first/last bins (we don't know exactly where the spectra start or stop,
1323 // thus, search for the first and last bin above 0.0 to constrain the range
1324 Int_t firstBin = TMath::Max(1, FindFirstBinAboveIn3dSubset(fFractionHists[species], 0.0, jetPtBin, centBin));
1325 Int_t lastBin = TMath::Min(fFractionHists[species]->GetNbinsX(),
1326 FindLastBinAboveIn3dSubset(fFractionHists[species], 0.0, jetPtBin, centBin));
1327
1328 if (trackPt <= xAxis->GetBinCenter(firstBin)) {
1329 fraction = fFractionHists[species]->GetBinContent(firstBin, jetPtBin, centBin);
1330 fractionErrorStat = fFractionHists[species]->GetBinError(firstBin, jetPtBin, centBin);
1331 fractionErrorSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(firstBin, jetPtBin, centBin) : 0.;
1332 }
1333 else if (trackPt >= xAxis->GetBinCenter(lastBin)) {
1334 fraction = fFractionHists[species]->GetBinContent(lastBin, jetPtBin, centBin);
1335 fractionErrorStat = fFractionHists[species]->GetBinError(lastBin, jetPtBin, centBin);
1336 fractionErrorSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(lastBin, jetPtBin, centBin) : 0.;
1337 }
1338 else {
1339 Double_t x0 = 0., x1 = 0., y0 = 0., y1 = 0.;
1340 Double_t y0errStat = 0., y1errStat = 0., y0errSys = 0., y1errSys = 0.;
1341 Int_t trackPtBin = xAxis->FindBin(trackPt);
1342
1343 // Linear interpolation between nearest neighbours in trackPt
1344 if (trackPt <= xAxis->GetBinCenter(trackPtBin)) {
1345 y0 = fFractionHists[species]->GetBinContent(trackPtBin - 1, jetPtBin, centBin);
1346 y0errStat = fFractionHists[species]->GetBinError(trackPtBin - 1, jetPtBin, centBin);
1347 y0errSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(trackPtBin - 1, jetPtBin, centBin)
1348 : 0.;
1349 x0 = xAxis->GetBinCenter(trackPtBin - 1);
1350 y1 = fFractionHists[species]->GetBinContent(trackPtBin, jetPtBin, centBin);
1351 y1errStat = fFractionHists[species]->GetBinError(trackPtBin, jetPtBin, centBin);
1352 y1errSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(trackPtBin, jetPtBin, centBin)
1353 : 0.;
1354 x1 = xAxis->GetBinCenter(trackPtBin);
1355 }
1356 else {
1357 y0 = fFractionHists[species]->GetBinContent(trackPtBin, jetPtBin, centBin);
1358 y0errStat = fFractionHists[species]->GetBinError(trackPtBin, jetPtBin, centBin);
1359 y0errSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(trackPtBin, jetPtBin, centBin)
1360 : 0.;
1361 x0 = xAxis->GetBinCenter(trackPtBin);
1362 y1 = fFractionHists[species]->GetBinContent(trackPtBin + 1, jetPtBin, centBin);
1363 y1errStat = fFractionHists[species]->GetBinError(trackPtBin + 1, jetPtBin, centBin);
1364 y1errSys = fFractionSysErrorHists[species] ? fFractionSysErrorHists[species]->GetBinError(trackPtBin + 1, jetPtBin, centBin)
1365 : 0.;
1366 x1 = xAxis->GetBinCenter(trackPtBin + 1);
1367 }
1368
1369 // Per construction: x0 < trackPt < x1
1370 fraction = y0 + (trackPt - x0) * ((y1 - y0) / (x1 - x0));
1371 fractionErrorStat = y0errStat + (trackPt - x0) * ((y1errStat - y0errStat) / (x1 - x0));
1372 fractionErrorSys = fFractionSysErrorHists[species] ? (y0errSys + (trackPt - x0) * ((y1errSys - y0errSys) / (x1 - x0))) : 0.;
1373 }
1374
1375 return kTRUE;
1376}
1377
1378
1379//_____________________________________________________________________________
9d7ad2e4 1380Bool_t AliAnalysisTaskPID::GetParticleFractions(Double_t trackPt, Double_t jetPt, Double_t centralityPercentile,
1381 Double_t* prob, Int_t smearSpeciesByError,
1382 Int_t takeIntoAccountSpeciesSysError, Bool_t uniformSystematicError) const
e131b05f 1383{
1384 // Fills the particle fractions for the given trackPt, jetPt and centrality into "prob".
1385 // Use jetPt = -1 for inclusive spectra and centralityPercentile = -1 for pp.
1386 // If smearSpeciesByError is >= 0 && < AliPID::kSPECIES, the returned fractions will be a random number distributed
1387 // with a gauss with mean being the corresponding particle fraction and sigma it's error for the considered species
1388 // "smearSpeciesByError".
1389 // Note that in this case the fractions for all species will NOT sum up to 1!
1390 // Thus, all other species fractions will be re-scaled weighted with their corresponding statistical error.
1391 // A similar procedure is used for "takeIntoAccountSpeciesSysError": The systematic error of the corresponding species
1392 // is used to generate a random number with uniform distribution in [mean - sysError, mean + sysError] for the new mean
1393 // (in cace of uniformSystematicError = kTRUE, otherwise it will be a gaus(mean, sysError)),
1394 // then the other species will be re-scaled according to their systematic errors.
1395 // First, the systematic error uncertainty procedure will be performed (that is including re-scaling), then the statistical
1396 // uncertainty procedure.
1397 // On success, kTRUE is returned.
1398
1399 if (!prob || smearSpeciesByError >= AliPID::kSPECIES || takeIntoAccountSpeciesSysError >= AliPID::kSPECIES)
1400 return kFALSE;
1401
1402 Double_t probTemp[AliPID::kSPECIES];
1403 Double_t probErrorStat[AliPID::kSPECIES];
1404 Double_t probErrorSys[AliPID::kSPECIES];
1405
1406 Bool_t success = kTRUE;
1407 success = success && GetParticleFraction(trackPt, jetPt, centralityPercentile, AliPID::kElectron,
1408 probTemp[AliPID::kElectron], probErrorStat[AliPID::kElectron],
1409 probErrorSys[AliPID::kElectron]);
1410 success = success && GetParticleFraction(trackPt, jetPt, centralityPercentile, AliPID::kMuon,
1411 probTemp[AliPID::kMuon], probErrorStat[AliPID::kMuon], probErrorSys[AliPID::kMuon]);
1412 success = success && GetParticleFraction(trackPt, jetPt, centralityPercentile, AliPID::kPion,
1413 probTemp[AliPID::kPion], probErrorStat[AliPID::kPion], probErrorSys[AliPID::kPion]);
1414 success = success && GetParticleFraction(trackPt, jetPt, centralityPercentile, AliPID::kKaon,
1415 probTemp[AliPID::kKaon], probErrorStat[AliPID::kKaon], probErrorSys[AliPID::kKaon]);
1416 success = success && GetParticleFraction(trackPt, jetPt, centralityPercentile, AliPID::kProton,
1417 probTemp[AliPID::kProton], probErrorStat[AliPID::kProton], probErrorSys[AliPID::kProton]);
1418
1419 if (!success)
1420 return kFALSE;
1421
1422 // If desired, take into account the systematic error of the corresponding species and re-generate probTemp accordingly
1423 if (takeIntoAccountSpeciesSysError >= 0) {
1424 // Generate random fraction of the considered species "smearSpeciesByError" according to mean and sigma
1425 Double_t generatedFraction = uniformSystematicError
1426 ? fRandom->Rndm() * 2. * probErrorSys[takeIntoAccountSpeciesSysError]
1427 - probErrorSys[takeIntoAccountSpeciesSysError]
1428 + probTemp[takeIntoAccountSpeciesSysError]
1429 : fRandom->Gaus(probTemp[takeIntoAccountSpeciesSysError],
1430 probErrorSys[takeIntoAccountSpeciesSysError]);
1431
1432 // Catch cases with invalid fraction (can happen for large errors), i.e. fraction < 0 or > 1
1433 if (generatedFraction < 0.)
1434 generatedFraction = 0.;
1435 else if (generatedFraction > 1.)
1436 generatedFraction = 1.;
1437
1438 // Calculate difference from original fraction (original fractions sum up to 1!)
1439 Double_t deltaFraction = generatedFraction - probTemp[takeIntoAccountSpeciesSysError];
1440
1441 // Fractions must (including errors) lie inside [0,1] -> Adapt weights accordingly by setting the errors
1442 if (deltaFraction > 0) {
1443 // Some part will be SUBTRACTED from the other fractions
1444 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1445 if (probTemp[i] - probErrorSys[i] < 0)
1446 probErrorSys[i] = probTemp[i];
1447 }
1448 }
1449 else {
1450 // Some part will be ADDED to the other fractions
1451 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1452 if (probTemp[i] + probErrorSys[i] > 1)
1453 probErrorSys[i] = 1. - probTemp[i];
1454 }
1455 }
1456
1457 // Compute summed weight of all fractions except for the considered one
1458 Double_t summedWeight = 0.;
1459 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1460 if (i != takeIntoAccountSpeciesSysError)
1461 summedWeight += probErrorSys[i];
1462 }
1463
1464 // Compute the weight for the other species
1465 /*
1466 if (summedWeight <= 1e-13) {
1467 // If this happens for some reason (it should not!), just assume flat weight
1468 printf("Error: summedWeight (sys error) ~ 0 for trackPt %f, jetPt %f, centralityPercentile %f. Setting flat weight!\n",
1469 trackPt, jetPt, centralityPercentile);
1470 }*/
1471
1472 Double_t weight[AliPID::kSPECIES];
1473 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1474 if (i != takeIntoAccountSpeciesSysError) {
1475 if (summedWeight > 1e-13)
1476 weight[i] = probErrorSys[i] / summedWeight;
1477 else
1478 weight[i] = probErrorSys[i] / (AliPID::kSPECIES - 1);
1479 }
1480 }
1481
1482 // For the final generated fractions, set the generated value for the considered species
1483 // and the generated value minus delta times statistical weight
1484 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1485 if (i != takeIntoAccountSpeciesSysError)
1486 probTemp[i] = probTemp[i] - weight[i] * deltaFraction;
1487 else
1488 probTemp[i] = generatedFraction;
1489 }
1490 }
1491
1492 // Using the values of probTemp (either the original ones or those after taking into account the systematic error),
1493 // calculate the final fractions - if the statistical error is to be taken into account, smear the corresponding
1494 // fraction. If not, just write probTemp to the final result array.
1495 if (smearSpeciesByError >= 0) {
1496 // Generate random fraction of the considered species "smearSpeciesByError" according to mean and sigma
1497 Double_t generatedFraction = fRandom->Gaus(probTemp[smearSpeciesByError], probErrorStat[smearSpeciesByError]);
1498
1499 // Catch cases with invalid fraction (can happen for large errors), i.e. fraction < 0 or > 1
1500 if (generatedFraction < 0.)
1501 generatedFraction = 0.;
1502 else if (generatedFraction > 1.)
1503 generatedFraction = 1.;
1504
1505 // Calculate difference from original fraction (original fractions sum up to 1!)
1506 Double_t deltaFraction = generatedFraction - probTemp[smearSpeciesByError];
1507
1508 // Fractions must (including errors) lie inside [0,1] -> Adapt weights accordingly by setting the errors
1509 if (deltaFraction > 0) {
1510 // Some part will be SUBTRACTED from the other fractions
1511 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1512 if (probTemp[i] - probErrorStat[i] < 0)
1513 probErrorStat[i] = probTemp[i];
1514 }
1515 }
1516 else {
1517 // Some part will be ADDED to the other fractions
1518 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1519 if (probTemp[i] + probErrorStat[i] > 1)
1520 probErrorStat[i] = 1. - probTemp[i];
1521 }
1522 }
1523
1524 // Compute summed weight of all fractions except for the considered one
1525 Double_t summedWeight = 0.;
1526 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1527 if (i != smearSpeciesByError)
1528 summedWeight += probErrorStat[i];
1529 }
1530
1531 // Compute the weight for the other species
1532 /*
1533 if (summedWeight <= 1e-13) {
1534 // If this happens for some reason (it should not!), just assume flat weight
1535 printf("Error: summedWeight (stat error) ~ 0 for trackPt %f, jetPt %f, centralityPercentile %f. Setting flat weight!\n",
1536 trackPt, jetPt, centralityPercentile);
1537 }*/
1538
1539 Double_t weight[AliPID::kSPECIES];
1540 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1541 if (i != smearSpeciesByError) {
1542 if (summedWeight > 1e-13)
1543 weight[i] = probErrorStat[i] / summedWeight;
1544 else
1545 weight[i] = probErrorStat[i] / (AliPID::kSPECIES - 1);
1546 }
1547 }
1548
1549 // For the final generated fractions, set the generated value for the considered species
1550 // and the generated value minus delta times statistical weight
1551 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1552 if (i != smearSpeciesByError)
1553 prob[i] = probTemp[i] - weight[i] * deltaFraction;
1554 else
1555 prob[i] = generatedFraction;
1556 }
1557 }
1558 else {
1559 // Just take the generated values
1560 for (Int_t i = 0; i < AliPID::kSPECIES; i++)
1561 prob[i] = probTemp[i];
1562 }
1563
1564
1565 // Should already be normalised, but make sure that it really is:
1566 Double_t probSum = 0.;
1567 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1568 probSum += prob[i];
1569 }
1570
1571 if (probSum <= 0)
1572 return kFALSE;
1573
1574 if (TMath::Abs(probSum - 1.0) > 1e-4) {
1575 printf("Warning: Re-normalising sum of fractions: Sum is %e\n", probSum);
1576 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1577 prob[i] /= probSum;
1578 }
1579 }
1580
1581 return kTRUE;
1582}
1583
1584
1585//_____________________________________________________________________________
9d7ad2e4 1586const TH3D* AliAnalysisTaskPID::GetParticleFractionHisto(Int_t species, Bool_t sysError) const
e131b05f 1587{
1588 if (species < AliPID::kElectron || species > AliPID::kProton)
1589 return 0x0;
1590
1591 return sysError ? fFractionSysErrorHists[species] : fFractionHists[species];
1592}
1593
1594
1595//_____________________________________________________________________________
1596Double_t AliAnalysisTaskPID::GetMCStrangenessFactorCMS(Int_t motherPDG, Double_t motherGenPt)
1597{
1598 // Strangeness ratio MC/data as function of mother pt from CMS data in |eta|<2.0
1599 // -> Based on function in PWGJE/AliAnalysisTaskFragmentationFunction, which uses
1600 // the following data from CMS pp @ 7 TeV inclusive (JHEP 05 (2011) 064)
1601
1602 Double_t fac = 1;
1603
1604 const Int_t absMotherPDG = TMath::Abs(motherPDG);
1605
1606 if (absMotherPDG == 310 || absMotherPDG == 321) { // K0s / K+ / K-
1607 if (0.00 <= motherGenPt && motherGenPt < 0.20) fac = 0.768049;
1608 else if(0.20 <= motherGenPt && motherGenPt < 0.40) fac = 0.732933;
1609 else if(0.40 <= motherGenPt && motherGenPt < 0.60) fac = 0.650298;
1610 else if(0.60 <= motherGenPt && motherGenPt < 0.80) fac = 0.571332;
1611 else if(0.80 <= motherGenPt && motherGenPt < 1.00) fac = 0.518734;
1612 else if(1.00 <= motherGenPt && motherGenPt < 1.20) fac = 0.492543;
1613 else if(1.20 <= motherGenPt && motherGenPt < 1.40) fac = 0.482704;
1614 else if(1.40 <= motherGenPt && motherGenPt < 1.60) fac = 0.488056;
1615 else if(1.60 <= motherGenPt && motherGenPt < 1.80) fac = 0.488861;
1616 else if(1.80 <= motherGenPt && motherGenPt < 2.00) fac = 0.492862;
1617 else if(2.00 <= motherGenPt && motherGenPt < 2.20) fac = 0.504332;
1618 else if(2.20 <= motherGenPt && motherGenPt < 2.40) fac = 0.501858;
1619 else if(2.40 <= motherGenPt && motherGenPt < 2.60) fac = 0.512970;
1620 else if(2.60 <= motherGenPt && motherGenPt < 2.80) fac = 0.524131;
1621 else if(2.80 <= motherGenPt && motherGenPt < 3.00) fac = 0.539130;
1622 else if(3.00 <= motherGenPt && motherGenPt < 3.20) fac = 0.554101;
1623 else if(3.20 <= motherGenPt && motherGenPt < 3.40) fac = 0.560348;
1624 else if(3.40 <= motherGenPt && motherGenPt < 3.60) fac = 0.568869;
1625 else if(3.60 <= motherGenPt && motherGenPt < 3.80) fac = 0.583310;
1626 else if(3.80 <= motherGenPt && motherGenPt < 4.00) fac = 0.604818;
1627 else if(4.00 <= motherGenPt && motherGenPt < 5.00) fac = 0.632630;
1628 else if(5.00 <= motherGenPt && motherGenPt < 6.00) fac = 0.710070;
1629 else if(6.00 <= motherGenPt && motherGenPt < 8.00) fac = 0.736365;
1630 else if(8.00 <= motherGenPt && motherGenPt < 10.00) fac = 0.835865;
1631 }
1632
1633 if (absMotherPDG == 3122) { // Lambda
1634 if (0.00 <= motherGenPt && motherGenPt < 0.20) fac = 0.645162;
1635 else if(0.20 <= motherGenPt && motherGenPt < 0.40) fac = 0.627431;
1636 else if(0.40 <= motherGenPt && motherGenPt < 0.60) fac = 0.457136;
1637 else if(0.60 <= motherGenPt && motherGenPt < 0.80) fac = 0.384369;
1638 else if(0.80 <= motherGenPt && motherGenPt < 1.00) fac = 0.330597;
1639 else if(1.00 <= motherGenPt && motherGenPt < 1.20) fac = 0.309571;
1640 else if(1.20 <= motherGenPt && motherGenPt < 1.40) fac = 0.293620;
1641 else if(1.40 <= motherGenPt && motherGenPt < 1.60) fac = 0.283709;
1642 else if(1.60 <= motherGenPt && motherGenPt < 1.80) fac = 0.282047;
1643 else if(1.80 <= motherGenPt && motherGenPt < 2.00) fac = 0.277261;
1644 else if(2.00 <= motherGenPt && motherGenPt < 2.20) fac = 0.275772;
1645 else if(2.20 <= motherGenPt && motherGenPt < 2.40) fac = 0.280726;
1646 else if(2.40 <= motherGenPt && motherGenPt < 2.60) fac = 0.288540;
1647 else if(2.60 <= motherGenPt && motherGenPt < 2.80) fac = 0.288315;
1648 else if(2.80 <= motherGenPt && motherGenPt < 3.00) fac = 0.296619;
1649 else if(3.00 <= motherGenPt && motherGenPt < 3.20) fac = 0.302993;
1650 else if(3.20 <= motherGenPt && motherGenPt < 3.40) fac = 0.338121;
1651 else if(3.40 <= motherGenPt && motherGenPt < 3.60) fac = 0.349800;
1652 else if(3.60 <= motherGenPt && motherGenPt < 3.80) fac = 0.356802;
1653 else if(3.80 <= motherGenPt && motherGenPt < 4.00) fac = 0.391202;
1654 else if(4.00 <= motherGenPt && motherGenPt < 5.00) fac = 0.422573;
1655 else if(5.00 <= motherGenPt && motherGenPt < 6.00) fac = 0.573815;
1656 else if(6.00 <= motherGenPt && motherGenPt < 8.00) fac = 0.786984;
1657 else if(8.00 <= motherGenPt && motherGenPt < 10.00) fac = 1.020021;
1658 }
1659
1660 if (absMotherPDG == 3312 || absMotherPDG == 3322) { // xi
1661 if (0.00 <= motherGenPt && motherGenPt < 0.20) fac = 0.666620;
1662 else if(0.20 <= motherGenPt && motherGenPt < 0.40) fac = 0.575908;
1663 else if(0.40 <= motherGenPt && motherGenPt < 0.60) fac = 0.433198;
1664 else if(0.60 <= motherGenPt && motherGenPt < 0.80) fac = 0.340901;
1665 else if(0.80 <= motherGenPt && motherGenPt < 1.00) fac = 0.290896;
1666 else if(1.00 <= motherGenPt && motherGenPt < 1.20) fac = 0.236074;
1667 else if(1.20 <= motherGenPt && motherGenPt < 1.40) fac = 0.218681;
1668 else if(1.40 <= motherGenPt && motherGenPt < 1.60) fac = 0.207763;
1669 else if(1.60 <= motherGenPt && motherGenPt < 1.80) fac = 0.222848;
1670 else if(1.80 <= motherGenPt && motherGenPt < 2.00) fac = 0.208806;
1671 else if(2.00 <= motherGenPt && motherGenPt < 2.20) fac = 0.197275;
1672 else if(2.20 <= motherGenPt && motherGenPt < 2.40) fac = 0.183645;
1673 else if(2.40 <= motherGenPt && motherGenPt < 2.60) fac = 0.188788;
1674 else if(2.60 <= motherGenPt && motherGenPt < 2.80) fac = 0.188282;
1675 else if(2.80 <= motherGenPt && motherGenPt < 3.00) fac = 0.207442;
1676 else if(3.00 <= motherGenPt && motherGenPt < 3.20) fac = 0.240388;
1677 else if(3.20 <= motherGenPt && motherGenPt < 3.40) fac = 0.241916;
1678 else if(3.40 <= motherGenPt && motherGenPt < 3.60) fac = 0.208276;
1679 else if(3.60 <= motherGenPt && motherGenPt < 3.80) fac = 0.234550;
1680 else if(3.80 <= motherGenPt && motherGenPt < 4.00) fac = 0.251689;
1681 else if(4.00 <= motherGenPt && motherGenPt < 5.00) fac = 0.310204;
1682 else if(5.00 <= motherGenPt && motherGenPt < 6.00) fac = 0.343492;
1683 }
1684
1685 const Double_t weight = 1. / fac;
1686
1687 return weight;
1688}
1689
1690
1691//_____________________________________________________________________________
1692Double_t AliAnalysisTaskPID::GetMCStrangenessFactorCMS(AliMCEvent* mcEvent, AliMCParticle* daughter)
1693{
1694 // Strangeness ratio MC/data as function of mother pt from CMS data in |eta|<2.0
1695 // -> Based on function in PWGJE/AliAnalysisTaskFragmentationFunction
1696
1697 if (!mcEvent)
1698 return 1.;
1699
1700 AliMCParticle* currentMother = daughter;
1701 AliMCParticle* currentDaughter = daughter;
1702
1703
1704 // find first primary mother K0s, Lambda or Xi
1705 while(1) {
1706 Int_t daughterPDG = currentDaughter->PdgCode();
1707
1708 Int_t motherLabel = currentDaughter->GetMother();
1709 if(motherLabel >= mcEvent->GetNumberOfTracks()){ // protection
1710 currentMother = currentDaughter;
1711 break;
1712 }
1713
1714 currentMother = (AliMCParticle*)mcEvent->GetTrack(motherLabel);
1715
1716 if (!currentMother) {
1717 currentMother = currentDaughter;
1718 break;
1719 }
1720
1721 Int_t motherPDG = currentMother->PdgCode();
1722
1723 // phys. primary found ?
1724 if (mcEvent->IsPhysicalPrimary(motherLabel))
1725 break;
1726
1727 if (TMath::Abs(daughterPDG) == 321) {
1728 // K+/K- e.g. from phi (ref data not feeddown corrected)
1729 currentMother = currentDaughter;
1730 break;
1731 }
1732 if (TMath::Abs(motherPDG) == 310) {
1733 // K0s e.g. from phi (ref data not feeddown corrected)
1734 break;
1735 }
1736 if (TMath::Abs(motherPDG) == 3212 && TMath::Abs(daughterPDG) == 3122) {
1737 // Mother Sigma0, daughter Lambda (this case not included in feeddown corr.)
1738 currentMother = currentDaughter;
1739 break;
1740 }
1741
1742 currentDaughter = currentMother;
1743 }
1744
1745
1746 Int_t motherPDG = currentMother->PdgCode();
1747 Double_t motherGenPt = currentMother->Pt();
1748
1749 return GetMCStrangenessFactorCMS(motherPDG, motherGenPt);
1750}
1751
1752
77324970
CKB		 1753// _________________________________________________________________________________
1754AliAnalysisTaskPID::TOFpidInfo AliAnalysisTaskPID::GetTOFType(const AliVTrack* track, Int_t tofMode) const
1755{
1756 // Get the (locally defined) particle type judged by TOF
cadb37bc 1757
77324970
CKB		 1758 if (!fPIDResponse) {
1759 Printf("ERROR: fEvent not available -> Cannot determine TOF type!");
1760 return kNoTOFinfo;
1761 }
1762
1763 // Check kTOFout, kTIME, mismatch
1764 const AliPIDResponse::EDetPidStatus tofStatus = fPIDResponse->CheckPIDStatus(AliPIDResponse::kTOF, track);
1765 if (tofStatus != AliPIDResponse::kDetPidOk)
1766 return kNoTOFinfo;
cadb37bc 1767
1768 Double_t nsigma[kNumTOFspecies + 1] = { -999., -999., -999., -999. };
77324970
CKB		 1769 nsigma[kTOFpion] = fPIDResponse->NumberOfSigmasTOF(track, AliPID::kPion);
1770 nsigma[kTOFkaon] = fPIDResponse->NumberOfSigmasTOF(track, AliPID::kKaon);
1771 nsigma[kTOFproton] = fPIDResponse->NumberOfSigmasTOF(track, AliPID::kProton);
cadb37bc 1772
77324970
CKB		 1773 Double_t inclusion = -999;
1774 Double_t exclusion = -999;
1775
1776 if (tofMode == 0) {
bd5839c6 1777 inclusion = 3.;
1778 exclusion = 2.5;
77324970 1779 }
275a4969 1780 else if (tofMode == 1) { // default
bd5839c6 1781 inclusion = 3.;
1782 exclusion = 3.;
77324970
CKB		 1783 }
1784 else if (tofMode == 2) {
bd5839c6 1785 inclusion = 3.;
1786 exclusion = 3.5;
77324970
CKB		 1787 }
1788 else {
1789 Printf("ERROR: Bad TOF mode: %d!", tofMode);
1790 return kNoTOFinfo;
1791 }
cadb37bc 1792
bd5839c6 1793 // Do not cut on nSigma electron because this would also remove pions in a large pT range.
1794 // The electron contamination of the pion sample can be treated by TPC dEdx fits afterwards.
1795 if (TMath::Abs(nsigma[kTOFpion]) < inclusion && TMath::Abs(nsigma[kTOFkaon]) > exclusion && TMath::Abs(nsigma[kTOFproton]) > exclusion)
77324970 1796 return kTOFpion;
bd5839c6 1797 if (TMath::Abs(nsigma[kTOFpion]) > exclusion && TMath::Abs(nsigma[kTOFkaon]) < inclusion && TMath::Abs(nsigma[kTOFproton]) > exclusion)
77324970 1798 return kTOFkaon;
bd5839c6 1799 if (TMath::Abs(nsigma[kTOFpion]) > exclusion && TMath::Abs(nsigma[kTOFkaon]) > exclusion && TMath::Abs(nsigma[kTOFproton]) < inclusion)
77324970 1800 return kTOFproton;
cadb37bc 1801
1802 // There are no TOF electrons selected because the purity is rather bad, even for small momenta
1803 // (also a small mismatch probability significantly affects electrons because their fraction
1804 // is small). There is no need for TOF electrons anyway, since the dEdx distribution of kaons and
1805 // protons in a given pT bin (also at the dEdx crossings) is very different from that of electrons.
1806 // This is due to the steeply falling dEdx of p and K with momentum, whereas the electron dEdx stays
1807 // rather constant.
1808 // As a result, the TPC fit yields a more accurate electron fraction than the TOF selection can do.
1809
77324970
CKB		 1810 return kNoTOFpid;
1811}
1812
1813
e131b05f 1814// _________________________________________________________________________________
1815Bool_t AliAnalysisTaskPID::IsSecondaryWithStrangeMotherMC(AliMCEvent* mcEvent, Int_t partLabel)
1816{
1817 // Check whether particle is a secondary with strange mother, i.e. returns kTRUE if a strange mother is found
1818 // and the particle is NOT a physical primary. In all other cases kFALSE is returned
1819
1820 if (!mcEvent || partLabel < 0)
1821 return kFALSE;
1822
1823 AliMCParticle* part = (AliMCParticle*)mcEvent->GetTrack(partLabel);
1824
1825 if (!part)
1826 return kFALSE;
1827
1828 if (mcEvent->IsPhysicalPrimary(partLabel))
1829 return kFALSE;
1830
1831 Int_t iMother = part->GetMother();
1832 if (iMother < 0)
1833 return kFALSE;
1834
1835
1836 AliMCParticle* partM = (AliMCParticle*)mcEvent->GetTrack(iMother);
1837 if (!partM)
1838 return kFALSE;
1839
1840 Int_t codeM = TMath::Abs(partM->PdgCode());
1841 Int_t mfl = Int_t(codeM / TMath::Power(10, Int_t(TMath::Log10(codeM))));
1842 if (mfl == 3 && codeM != 3) // codeM = 3 is for s quark
1843 return kTRUE;
1844
1845 return kFALSE;
1846}
1847
1848
1849//_____________________________________________________________________________
9d7ad2e4 1850Bool_t AliAnalysisTaskPID::SetParticleFractionHisto(const TH3D* hist, Int_t species, Bool_t sysError)
e131b05f 1851{
1852 // Store a clone of hist (containing the particle fractions of the corresponding species with statistical error (sysError = kFALSE)
1853 // or systematic error (sysError = kTRUE), respectively), internally
1854
1855 if (species < AliPID::kElectron || species > AliPID::kProton) {
1856 AliError(Form("Only fractions for species index %d to %d can be set, but not for the requested one: %d", 0,
1857 AliPID::kProton, species));
1858 return kFALSE;
1859 }
1860
1861 if (sysError) {
1862 delete fFractionSysErrorHists[species];
1863
1864 fFractionSysErrorHists[species] = new TH3D(*hist);
1865 }
1866 else {
1867 delete fFractionHists[species];
1868
1869 fFractionHists[species] = new TH3D(*hist);
1870 }
1871
1872 return kTRUE;
1873}
1874
1875
1876//_____________________________________________________________________________
9d7ad2e4 1877Bool_t AliAnalysisTaskPID::SetParticleFractionHistosFromFile(const TString filePathName, Bool_t sysError)
e131b05f 1878{
1879 // Loads particle fractions for all species from the desired file and returns kTRUE on success.
1880 // The maps are assumed to be of Type TH3D, to sit in the main directory and to have names
1881 // Form("hFraction_%e", AliPID::ParticleName(i)) for sysError = kFALSE and
1882 // Form("hFractionSysError_%e", AliPID::ParticleName(i)) for sysError = kTRUE.
1883
1884 TFile* f = TFile::Open(filePathName.Data());
1885 if (!f) {
1886 std::cout << "Failed to open file with particle fractions \"" << filePathName.Data() << "\"!" << std::endl;
1887 return kFALSE;
1888 }
1889
1890 TH3D* hist = 0x0;
1891 for (Int_t i = 0; i < AliPID::kSPECIES; i++) {
1892 TString histName = Form("hFraction%s_%s", sysError ? "SysError" : "", AliPID::ParticleName(i));
1893 hist = dynamic_cast<TH3D*>(f->Get(histName.Data()));
1894 if (!hist) {
1895 std::cout << "Failed to load particle fractions for " << histName.Data() << "!";
1896 std::cout << std::endl << "Cleaning up particle fraction histos!" << std::endl;
1897 CleanupParticleFractionHistos();
1898 return kFALSE;
1899 }
1900
1901 if (!SetParticleFractionHisto(hist, i, sysError)) {
1902 std::cout << "Failed to load particle fractions for " << histName.Data() << "!";
1903 std::cout << std::endl << "Cleaning up particle fraction histos!" << std::endl;
1904 CleanupParticleFractionHistos();
1905 return kFALSE;
1906 }
1907 }
1908
1909 delete hist;
1910
1911 return kTRUE;
1912
1913}
1914
1915
1916//_____________________________________________________________________________
9d7ad2e4 1917Int_t AliAnalysisTaskPID::GetRandomParticleTypeAccordingToParticleFractions(Double_t trackPt, Double_t jetPt,
1918 Double_t centralityPercentile,
1919 Bool_t smearByError,
1920 Bool_t takeIntoAccountSysError) const
e131b05f 1921{
1922 // Uses the stored histograms with the particle fractions to generate a random particle type according to these fractions.
1923 // In case of problems (e.g. histo missing), AliPID::kUnknown is returned.
1924 // If smearByError is kTRUE, the used fractions will be random numbers distributed with a gauss with mean
1925 // being the corresponding particle fraction and sigma it's error.
1926 // Note that in this case only the fraction of a random species is varied in this way. The other fractions
1927 // will be re-normalised according their statistical errors.
1928 // The same holds for the systematic error of species "takeIntoAccountSpeciesSysError", but the random number will be
1929 // uniformly distributed within [mean - sys, mean + sys] and the re-normalisation will be weighted with the systematic errors.
1930 // Note that the fractions will be calculated first with only the systematic error taken into account (if desired), including
1931 // re-normalisation. Then, the resulting fractions will be used to calculate the final fractions - either with statistical error
1932 // or without. The species, for which the error will be used for smearing, is the same for sys and stat error.
1933
1934 Double_t prob[AliPID::kSPECIES];
1935 Int_t randomSpecies = (smearByError || takeIntoAccountSysError) ? (Int_t)(fRandom->Rndm() * AliPID::kSPECIES) : -1;
1936 Bool_t success = GetParticleFractions(trackPt, jetPt, centralityPercentile, prob, randomSpecies, randomSpecies);
1937
1938 if (!success)
1939 return AliPID::kUnknown;
1940
1941 Double_t rnd = fRandom->Rndm(); // Produce uniformly distributed floating point in ]0, 1]
1942
1943 if (rnd <= prob[AliPID::kPion])
1944 return AliPID::kPion;
1945 else if (rnd <= prob[AliPID::kPion] + prob[AliPID::kKaon])
1946 return AliPID::kKaon;
1947 else if (rnd <= prob[AliPID::kPion] + prob[AliPID::kKaon] + prob[AliPID::kProton])
1948 return AliPID::kProton;
1949 else if (rnd <= prob[AliPID::kPion] + prob[AliPID::kKaon] + prob[AliPID::kProton] + prob[AliPID::kElectron])
1950 return AliPID::kElectron;
1951
1952 return AliPID::kMuon; //else it must be a muon (only species left)
1953}
1954
1955
1956//_____________________________________________________________________________
9d7ad2e4 1957AliAnalysisTaskPID::ErrorCode AliAnalysisTaskPID::GenerateDetectorResponse(AliAnalysisTaskPID::ErrorCode errCode,
1958 Double_t mean, Double_t sigma,
1959 Double_t* responses, Int_t nResponses,
1960 Bool_t usePureGaus)
e131b05f 1961{
1962 // Generate detector response. If a previous generation was not successful or there is something wrong with this signal generation,
1963 // the function will return kFALSE
1964 if (!responses)
1965 return kError;
1966
1967 // Reset response array
1968 for (Int_t i = 0; i < nResponses; i++)
1969 responses[i] = -999;
1970
1971 if (errCode == kError)
1972 return kError;
1973
1974 ErrorCode ownErrCode = kNoErrors;
1975
1976 if (fUseConvolutedGaus && !usePureGaus) {
1977 // In case of convoluted gauss, calculate the probability density only once to save a lot of time!
1978
1979 TH1* hProbDensity = 0x0;
1980 ownErrCode = SetParamsForConvolutedGaus(mean, sigma);
1981 if (ownErrCode == kError)
1982 return kError;
1983
1984 hProbDensity = fConvolutedGausDeltaPrime->GetHistogram();
1985
1986 for (Int_t i = 0; i < nResponses; i++) {
1987 responses[i] = hProbDensity->GetRandom();
1988 //responses[i] fConvolutedGausDeltaPrime->GetRandom(); // MUCH slower than using the binned version via the histogram
1989 }
1990 }
1991 else {
1992 for (Int_t i = 0; i < nResponses; i++) {
1993 responses[i] = fRandom->Gaus(mean, sigma);
1994 }
1995 }
1996
1997 // If forwarded error code was a warning (error case has been handled before), return a warning
1998 if (errCode == kWarning)
1999 return kWarning;
2000
2001 return ownErrCode; // Forward success/warning
2002}
2003
2004
2005//_____________________________________________________________________________
2006void AliAnalysisTaskPID::PrintSettings(Bool_t printSystematicsSettings) const
2007{
2008 // Print current settings.
2009
2010 printf("\n\nSettings for task %s:\n", GetName());
2011 printf("Is pPb/Pbp: %d -> %s\n", GetIsPbpOrpPb(), GetIsPbpOrpPb() ? "Adapting vertex cuts" : "Using standard vertex cuts");
2012 printf("Track cuts: %s\n", fTrackFilter ? fTrackFilter->GetTitle() : "-");
2013 printf("Eta cut: %.2f <= |eta| <= %.2f\n", GetEtaAbsCutLow(), GetEtaAbsCutUp());
2014 printf("Phi' cut: %d\n", GetUsePhiCut());
a6852ea8 2015 printf("TPCCutMIGeo: %d\n", GetUseTPCCutMIGeo());
493982d9
ML		 2016 if (GetUseTPCCutMIGeo()) {
2017 printf("GetCutGeo: %f\n", GetCutGeo());
2018 printf("GetCutNcr: %f\n", GetCutNcr());
2019 printf("GetCutNcl: %f\n", GetCutNcl());
2020 }
2021 printf("TPCnclCut: %d\n", GetUseTPCnclCut());
2022 if (GetUseTPCnclCut()) {
2023 printf("GetCutPureNcl: %d\n", GetCutPureNcl());
2024 }
e131b05f 2025
2026 printf("\n");
2027
2028 printf("Centrality estimator: %s\n", GetCentralityEstimator().Data());
2029
2030 printf("\n");
2031
2032 printf("Use MC-ID for signal generation: %d\n", GetUseMCidForGeneration());
2033 printf("Use ITS: %d\n", GetUseITS());
2034 printf("Use TOF: %d\n", GetUseTOF());
2035 printf("Use priors: %d\n", GetUsePriors());
2036 printf("Use TPC default priors: %d\n", GetUseTPCDefaultPriors());
2037 printf("Use convoluted Gauss: %d\n", GetUseConvolutedGaus());
2038 printf("Accuracy of non-Gaussian tail: %e\n", GetAccuracyNonGaussianTail());
2039 printf("Take into account muons: %d\n", GetTakeIntoAccountMuons());
77324970 2040 printf("TOF mode: %d\n", GetTOFmode());
e131b05f 2041 printf("\nParams for transition from gauss to asymmetric shape:\n");
2042 printf("[0]: %e\n", GetConvolutedGaussTransitionPar(0));
2043 printf("[1]: %e\n", GetConvolutedGaussTransitionPar(1));
2044 printf("[2]: %e\n", GetConvolutedGaussTransitionPar(2));
2045
9e95a906 2046 printf("\n");
2047
2048 printf("Do PID: %d\n", fDoPID);
2049 printf("Do Efficiency: %d\n", fDoEfficiency);
2050 printf("Do PtResolution: %d\n", fDoPtResolution);
2051
e131b05f 2052 printf("\n");
2053
2054 printf("Input from other task: %d\n", GetInputFromOtherTask());
2055 printf("Store additional jet information: %d\n", GetStoreAdditionalJetInformation());
2056 printf("Store centrality percentile: %d", GetStoreCentralityPercentile());
2057
2058 if (printSystematicsSettings)
2059 PrintSystematicsSettings();
2060 else
2061 printf("\n\n\n");
2062}
2063
2064
2065//_____________________________________________________________________________
2066void AliAnalysisTaskPID::PrintSystematicsSettings() const
2067{
2068 // Print current settings for systematic studies.
2069
2070 printf("\n\nSettings for systematics for task %s:\n", GetName());
21b0adb1 2071 printf("SplinesThr:\t%f\n", GetSystematicScalingSplinesThreshold());
2072 printf("SplinesBelowThr:\t%f\n", GetSystematicScalingSplinesBelowThreshold());
2073 printf("SplinesAboveThr:\t%f\n", GetSystematicScalingSplinesAboveThreshold());
e131b05f 2074 printf("EtaCorrMomThr:\t%f\n", GetSystematicScalingEtaCorrectionMomentumThr());
2075 printf("EtaCorrLowP:\t%f\n", GetSystematicScalingEtaCorrectionLowMomenta());
2076 printf("EtaCorrHighP:\t%f\n", GetSystematicScalingEtaCorrectionHighMomenta());
5709c40a 2077 printf("SigmaParaThr:\t%f\n", GetSystematicScalingEtaSigmaParaThreshold());
2078 printf("SigmaParaBelowThr:\t%f\n", GetSystematicScalingEtaSigmaParaBelowThreshold());
2079 printf("SigmaParaAboveThr:\t%f\n", GetSystematicScalingEtaSigmaParaAboveThreshold());
e131b05f 2080 printf("MultCorr:\t%f\n", GetSystematicScalingMultCorrection());
71c60990 2081 printf("TOF mode: %d\n", GetTOFmode());
e131b05f 2082
2083 printf("\n\n");
2084}
2085
2086
2087//_____________________________________________________________________________
2088Bool_t AliAnalysisTaskPID::ProcessTrack(const AliVTrack* track, Int_t particlePDGcode, Double_t centralityPercentile,
2089 Double_t jetPt)
2090{
2091 // Process the track (generate expected response, fill histos, etc.).
2092 // particlePDGcode == 0 means data. Otherwise, the corresponding MC ID will be assumed.
2093
2094 //Printf("Debug: Task %s is starting to process track: dEdx %f, pTPC %f, eta %f, ncl %d\n", GetName(), track->GetTPCsignal(), track->GetTPCmomentum(),
2095 // track->Eta(), track->GetTPCsignalN());
2096
9e95a906 2097 if(fDebug > 1)
2098 printf("File: %s, Line: %d: ProcessTrack\n", (char*)__FILE__, __LINE__);
2099
2100 if (!fDoPID)
2101 return kFALSE;
2102
2103 if(fDebug > 2)
2104 printf("File: %s, Line: %d: ProcessTrack -> Processing started\n", (char*)__FILE__, __LINE__);
2105
e131b05f 2106 const Bool_t isMC = (particlePDGcode == 0) ? kFALSE : kTRUE;
2107
2108 Int_t binMC = -1;
2109
2110 if (isMC) {
2111 if (TMath::Abs(particlePDGcode) == 211) {//Pion
2112 binMC = 3;
2113 }
2114 else if (TMath::Abs(particlePDGcode) == 321) {//Kaon
2115 binMC = 1;
2116 }
2117 else if (TMath::Abs(particlePDGcode) == 2212) {//Proton
2118 binMC = 4;
2119 }
2120 else if (TMath::Abs(particlePDGcode) == 11) {//Electron
2121 binMC = 0;
2122 }
2123 else if (TMath::Abs(particlePDGcode) == 13) {//Muon
2124 binMC = 2;
2125 }
2126 else // In MC-ID case, set to underflow bin such that the response from this track is only used for unidentified signal generation
2127 // or signal generation with PID response and the track is still there (as in data) - e.g. relevant w.r.t. deuterons.
2128 // This is important to be as much as possible consistent with data. And the tracks can still be removed by disabling the
2129 // underflow bin for the projections
2130 binMC = -1;
2131 }
2132
2133 // Momenta
2134 //Double_t p = track->GetP();
44ed76dd 2135 const Double_t pTPC = track->GetTPCmomentum();
e131b05f 2136 Double_t pT = track->Pt();
2137
2138 Double_t z = -1, xi = -1;
2139 GetJetTrackObservables(pT, jetPt, z, xi);
2140
2141
2142 Double_t trackCharge = track->Charge();
2143
2144 // TPC signal
2145 Double_t dEdxTPC = fPIDResponse->IsTunedOnData() ? fPIDResponse->GetTPCsignalTunedOnData(track) : track->GetTPCsignal();
2146
2147 if (dEdxTPC <= 0) {
44ed76dd 2148 Printf("Skipping track with strange dEdx value: dEdx %f, pTPC %f, eta %f, ncl %d\n", track->GetTPCsignal(), pTPC,
e131b05f 2149 track->Eta(), track->GetTPCsignalN());
2150 return kFALSE;
2151 }
2152
44ed76dd 2153 // Completely remove tracks from light nuclei defined by el and pr <dEdx> as function of pT.
2154 // A very loose cut to be sure not to throw away electrons and/or protons
2155 Double_t nSigmaPr = fPIDResponse->NumberOfSigmasTPC(track, AliPID::kProton);
2156 Double_t nSigmaEl = fPIDResponse->NumberOfSigmasTPC(track, AliPID::kElectron);
e131b05f 2157
44ed76dd 2158 if ((pTPC >= 0.3 && (nSigmaPr > 10 && nSigmaEl > 10)) ||
2159 (pTPC < 0.3 && (nSigmaPr > 15 && nSigmaEl > 15))) {
2160 Printf("Skipping track which seems to be a light nuclei: dEdx %f, pTPC %f, pT %f, eta %f, ncl %d, nSigmaPr %f, nSigmaEl %f\n",
2161 track->GetTPCsignal(), pTPC, pT, track->Eta(), track->GetTPCsignalN(), nSigmaPr, nSigmaEl);
2162 return kFALSE;
2163 }
e131b05f 2164
2165
2166 Double_t dEdxEl, dEdxKa, dEdxPi, dEdxMu, dEdxPr;
2167 Double_t sigmaEl, sigmaKa, sigmaPi, sigmaMu, sigmaPr;
2168
2169 if (fDoAnySystematicStudiesOnTheExpectedSignal) {
2170 // Get the uncorrected signal first and the corresponding correction factors.
2171 // Then modify the correction factors and properly recalculate the corrected dEdx
2172
2173 // Get pure spline values for dEdx_expected, without any correction
2174 dEdxEl = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kElectron, AliTPCPIDResponse::kdEdxDefault, kFALSE, kFALSE);
2175 dEdxKa = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kKaon, AliTPCPIDResponse::kdEdxDefault, kFALSE, kFALSE);
2176 dEdxPi = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kPion, AliTPCPIDResponse::kdEdxDefault, kFALSE, kFALSE);
2177 dEdxMu = !fTakeIntoAccountMuons ? -1 :
2178 fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kMuon, AliTPCPIDResponse::kdEdxDefault, kFALSE, kFALSE);
2179 dEdxPr = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kProton, AliTPCPIDResponse::kdEdxDefault, kFALSE, kFALSE);
2180
2181 // Scale splines, if desired
21b0adb1 2182 if ((TMath::Abs(fSystematicScalingSplinesBelowThreshold - 1.0) > fgkEpsilon) ||
2183 (TMath::Abs(fSystematicScalingSplinesAboveThreshold - 1.0) > fgkEpsilon)) {
2184
2185 // Tune turn-on of correction for pions (not so relevant for the other species, since at very large momenta)
21b0adb1 2186 Double_t bg = 0;
2187 Double_t scaleFactor = 1.;
2188 Double_t usedSystematicScalingSplines = 1.;
2189
2190 bg = pTPC / AliPID::ParticleMass(AliPID::kElectron);
2191 scaleFactor = 0.5 * (1. + TMath::Erf((bg - fSystematicScalingSplinesThreshold) / 4.));
2192 usedSystematicScalingSplines = fSystematicScalingSplinesBelowThreshold * (1 - scaleFactor)
2193 + fSystematicScalingSplinesAboveThreshold * scaleFactor;
2194 dEdxEl *= usedSystematicScalingSplines;
2195
2196 bg = pTPC / AliPID::ParticleMass(AliPID::kKaon);
2197 scaleFactor = 0.5 * (1. + TMath::Erf((bg - fSystematicScalingSplinesThreshold) / 4.));
2198 usedSystematicScalingSplines = fSystematicScalingSplinesBelowThreshold * (1 - scaleFactor)
2199 + fSystematicScalingSplinesAboveThreshold * scaleFactor;
2200 dEdxKa *= usedSystematicScalingSplines;
2201
2202 bg = pTPC / AliPID::ParticleMass(AliPID::kPion);
2203 scaleFactor = 0.5 * (1. + TMath::Erf((bg - fSystematicScalingSplinesThreshold) / 4.));
2204 usedSystematicScalingSplines = fSystematicScalingSplinesBelowThreshold * (1 - scaleFactor)
2205 + fSystematicScalingSplinesAboveThreshold * scaleFactor;
2206 dEdxPi *= usedSystematicScalingSplines;
2207
2208 if (fTakeIntoAccountMuons) {
2209 bg = pTPC / AliPID::ParticleMass(AliPID::kMuon);
2210 scaleFactor = 0.5 * (1. + TMath::Erf((bg - fSystematicScalingSplinesThreshold) / 4.));
2211 usedSystematicScalingSplines = fSystematicScalingSplinesBelowThreshold * (1 - scaleFactor)
2212 + fSystematicScalingSplinesAboveThreshold * scaleFactor;
2213 dEdxMu *= usedSystematicScalingSplines;
2214 }
2215
2216
2217 bg = pTPC / AliPID::ParticleMass(AliPID::kProton);
2218 scaleFactor = 0.5 * (1. + TMath::Erf((bg - fSystematicScalingSplinesThreshold) / 4.));
2219 usedSystematicScalingSplines = fSystematicScalingSplinesBelowThreshold * (1 - scaleFactor)
2220 + fSystematicScalingSplinesAboveThreshold * scaleFactor;
2221 dEdxPr *= usedSystematicScalingSplines;
e131b05f 2222 }
2223
2224 // Get the eta correction factors for the (modified) expected dEdx
2225 Double_t etaCorrEl = fPIDResponse->UseTPCEtaCorrection() ? fPIDResponse->GetTPCResponse().GetEtaCorrectionFast(track, dEdxEl) : 1.;
2226 Double_t etaCorrKa = fPIDResponse->UseTPCEtaCorrection() ? fPIDResponse->GetTPCResponse().GetEtaCorrectionFast(track, dEdxKa) : 1.;
2227 Double_t etaCorrPi = fPIDResponse->UseTPCEtaCorrection() ? fPIDResponse->GetTPCResponse().GetEtaCorrectionFast(track, dEdxPi) : 1.;
2228 Double_t etaCorrMu = fTakeIntoAccountMuons && !fPIDResponse->UseTPCEtaCorrection() ?
2229 fPIDResponse->GetTPCResponse().GetEtaCorrectionFast(track, dEdxMu) : 1.;
2230 Double_t etaCorrPr = fPIDResponse->UseTPCEtaCorrection() ? fPIDResponse->GetTPCResponse().GetEtaCorrectionFast(track, dEdxPr) : 1.;
2231
2232 // Scale eta correction factors, if desired (and eta correction maps are to be used, otherwise it is not possible!)
2233 if (fPIDResponse->UseTPCEtaCorrection() &&
2234 (TMath::Abs(fSystematicScalingEtaCorrectionHighMomenta - 1.0) > fgkEpsilon ||
2235 TMath::Abs(fSystematicScalingEtaCorrectionLowMomenta - 1.0) > fgkEpsilon)) {
2236 // Since we do not want to scale the splines with this, but only the eta variation, only scale the deviation of the correction factor!
2237 // E.g. if we would have a flat eta depence fixed at 1.0, we would shift the whole thing equal to shifting the splines by the same factor!
2238
2239
2240 // Due to additional azimuthal effects, there is an additional eta dependence for low momenta which is not corrected successfully so far.
2241 // One can assign a different (higher) systematic scale factor for this low-p region and a threshold which separates low- and high-p.
5709c40a 2242 // An ERF will be used to get (as a function of p_TPC) from one correction factor to the other within roughly 0.2 GeV/c
e131b05f 2243 Double_t usedSystematicScalingEtaCorrection = fSystematicScalingEtaCorrectionHighMomenta;
2244
2245 if (TMath::Abs(fSystematicScalingEtaCorrectionHighMomenta - fSystematicScalingEtaCorrectionLowMomenta) > fgkEpsilon) {
e131b05f 2246 const Double_t fractionHighMomentumScaleFactor = 0.5 * (1. + TMath::Erf((pTPC - fSystematicScalingEtaCorrectionMomentumThr) / 0.1));
2247 usedSystematicScalingEtaCorrection = fSystematicScalingEtaCorrectionLowMomenta * (1 - fractionHighMomentumScaleFactor)
2248 + fSystematicScalingEtaCorrectionHighMomenta * fractionHighMomentumScaleFactor;
2249 }
2250
2251 etaCorrEl = 1.0 + usedSystematicScalingEtaCorrection * (etaCorrEl - 1.0);
2252 etaCorrKa = 1.0 + usedSystematicScalingEtaCorrection * (etaCorrKa - 1.0);
2253 etaCorrPi = 1.0 + usedSystematicScalingEtaCorrection * (etaCorrPi - 1.0);
2254 etaCorrMu = fTakeIntoAccountMuons ? (1.0 + usedSystematicScalingEtaCorrection * (etaCorrMu - 1.0)) : 1.0;
2255 etaCorrPr = 1.0 + usedSystematicScalingEtaCorrection * (etaCorrPr - 1.0);
2256 }
2257
2258 // Get the multiplicity correction factors for the (modified) expected dEdx
2259 const Int_t currEvtMultiplicity = fPIDResponse->GetTPCResponse().GetCurrentEventMultiplicity();
2260
2261 Double_t multiplicityCorrEl = fPIDResponse->UseTPCMultiplicityCorrection() ? fPIDResponse->GetTPCResponse().GetMultiplicityCorrectionFast(track,
2262 dEdxEl * etaCorrEl, currEvtMultiplicity) : 1.;
2263 Double_t multiplicityCorrKa = fPIDResponse->UseTPCMultiplicityCorrection() ? fPIDResponse->GetTPCResponse().GetMultiplicityCorrectionFast(track,
2264 dEdxKa * etaCorrKa, currEvtMultiplicity) : 1.;
2265 Double_t multiplicityCorrPi = fPIDResponse->UseTPCMultiplicityCorrection() ? fPIDResponse->GetTPCResponse().GetMultiplicityCorrectionFast(track,
2266 dEdxPi * etaCorrPi, currEvtMultiplicity) : 1.;
2267 Double_t multiplicityCorrMu = fTakeIntoAccountMuons && fPIDResponse->UseTPCMultiplicityCorrection() ?
2268 fPIDResponse->GetTPCResponse().GetMultiplicityCorrectionFast(track, dEdxMu * etaCorrMu, currEvtMultiplicity) : 1.;
2269 Double_t multiplicityCorrPr = fPIDResponse->UseTPCMultiplicityCorrection() ? fPIDResponse->GetTPCResponse().GetMultiplicityCorrectionFast(track,
2270 dEdxPr * etaCorrPr, currEvtMultiplicity) : 1.;
2271
2272 Double_t multiplicityCorrSigmaEl = fPIDResponse->UseTPCMultiplicityCorrection() ?
2273 fPIDResponse->GetTPCResponse().GetMultiplicitySigmaCorrectionFast(dEdxEl * etaCorrEl, currEvtMultiplicity) : 1.;
2274 Double_t multiplicityCorrSigmaKa = fPIDResponse->UseTPCMultiplicityCorrection() ?
2275 fPIDResponse->GetTPCResponse().GetMultiplicitySigmaCorrectionFast(dEdxKa * etaCorrKa, currEvtMultiplicity) : 1.;
2276 Double_t multiplicityCorrSigmaPi = fPIDResponse->UseTPCMultiplicityCorrection() ?
2277 fPIDResponse->GetTPCResponse().GetMultiplicitySigmaCorrectionFast(dEdxPi * etaCorrPi, currEvtMultiplicity) : 1.;
2278 Double_t multiplicityCorrSigmaMu = fTakeIntoAccountMuons && fPIDResponse->UseTPCMultiplicityCorrection() ?
2279 fPIDResponse->GetTPCResponse().GetMultiplicitySigmaCorrectionFast(dEdxMu * etaCorrMu, currEvtMultiplicity) : 1.;
2280 Double_t multiplicityCorrSigmaPr = fPIDResponse->UseTPCMultiplicityCorrection() ?
2281 fPIDResponse->GetTPCResponse().GetMultiplicitySigmaCorrectionFast(dEdxPr * etaCorrPr, currEvtMultiplicity) : 1.;
2282
2283 // Scale multiplicity correction factors, if desired (and multiplicity correction functions are to be used, otherwise it is not possible!)
2284 if (fPIDResponse->UseTPCMultiplicityCorrection() && TMath::Abs(fSystematicScalingMultCorrection - 1.0) > fgkEpsilon) {
2285 // Since we do not want to scale the splines with this, but only the multiplicity variation, only scale the deviation of the correction factor!
2286 // E.g. if we would have a flat mult depence fix at 1.0, we would shift the whole thing equal to shifting the splines by the same factor!
2287
2288 multiplicityCorrEl = 1.0 + fSystematicScalingMultCorrection * (multiplicityCorrEl - 1.0);
2289 multiplicityCorrKa = 1.0 + fSystematicScalingMultCorrection * (multiplicityCorrKa - 1.0);
2290 multiplicityCorrPi = 1.0 + fSystematicScalingMultCorrection * (multiplicityCorrPi - 1.0);
2291 multiplicityCorrMu = fTakeIntoAccountMuons ? (1.0 + fSystematicScalingMultCorrection * (multiplicityCorrMu - 1.0)) : 1.0;
2292 multiplicityCorrPr = 1.0 + fSystematicScalingMultCorrection * (multiplicityCorrPr - 1.0);
2293 }
2294
2295 // eta correction must be enabled in order to use the new sigma parametrisation maps. Since this is the absolute sigma
2296 // for a track calculated with the unscaled paramaters, we have to devide out dEdxExpectedEtaCorrected and then need
2297 // to scale with the multiplicitySigmaCorrFactor * fSystematicScalingEtaSigmaPara. In the end, one has to scale with the
2298 // (modified) dEdx to get the absolute sigma
2299 // This means there is no extra parameter for the multiplicitySigmaCorrFactor, but only for the sigma map itself.
2300 // This is valid, since it appears only as a product. One has to assume a larger systematic shift in case of additional
2301 // multiplicity dependence....
e131b05f 2302
5709c40a 2303 Bool_t doSigmaSystematics = (TMath::Abs(fSystematicScalingEtaSigmaParaBelowThreshold - 1.0) > fgkEpsilon) ||
2304 (TMath::Abs(fSystematicScalingEtaSigmaParaAboveThreshold - 1.0) > fgkEpsilon);
e131b05f 2305
e131b05f 2306
5709c40a 2307 Double_t dEdxElExpected = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kElectron, AliTPCPIDResponse::kdEdxDefault,
2308 kTRUE, kFALSE);
2309 Double_t systematicScalingEtaSigmaParaEl = 1.;
2310 if (doSigmaSystematics) {
2311 Double_t scaleFactor = 0.5 * (1. + TMath::Erf((dEdxElExpected - fSystematicScalingEtaSigmaParaThreshold) / 25.));
2312 systematicScalingEtaSigmaParaEl = fSystematicScalingEtaSigmaParaBelowThreshold * (1 - scaleFactor)
2313 + fSystematicScalingEtaSigmaParaAboveThreshold * scaleFactor;
2314 }
2315 Double_t sigmaRelEl = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kElectron, AliTPCPIDResponse::kdEdxDefault,
2316 kTRUE, kFALSE)
2317 / dEdxElExpected * systematicScalingEtaSigmaParaEl * multiplicityCorrSigmaEl;
2318
2319
2320 Double_t dEdxKaExpected = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kKaon, AliTPCPIDResponse::kdEdxDefault,
2321 kTRUE, kFALSE);
2322 Double_t systematicScalingEtaSigmaParaKa = 1.;
2323 if (doSigmaSystematics) {
2324 Double_t scaleFactor = 0.5 * (1. + TMath::Erf((dEdxKaExpected - fSystematicScalingEtaSigmaParaThreshold) / 25.));
2325 systematicScalingEtaSigmaParaKa = fSystematicScalingEtaSigmaParaBelowThreshold * (1 - scaleFactor)
2326 + fSystematicScalingEtaSigmaParaAboveThreshold * scaleFactor;
2327 }
2328 Double_t sigmaRelKa = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kKaon, AliTPCPIDResponse::kdEdxDefault,
2329 kTRUE, kFALSE)
2330 / dEdxKaExpected * systematicScalingEtaSigmaParaKa * multiplicityCorrSigmaKa;
2331
2332
2333 Double_t dEdxPiExpected = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kPion, AliTPCPIDResponse::kdEdxDefault,
2334 kTRUE, kFALSE);
2335 Double_t systematicScalingEtaSigmaParaPi = 1.;
2336 if (doSigmaSystematics) {
2337 Double_t scaleFactor = 0.5 * (1. + TMath::Erf((dEdxPiExpected - fSystematicScalingEtaSigmaParaThreshold) / 25.));
2338 systematicScalingEtaSigmaParaPi = fSystematicScalingEtaSigmaParaBelowThreshold * (1 - scaleFactor)
2339 + fSystematicScalingEtaSigmaParaAboveThreshold * scaleFactor;
2340 }
2341 Double_t sigmaRelPi = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kPion, AliTPCPIDResponse::kdEdxDefault,
2342 kTRUE, kFALSE)
2343 / dEdxPiExpected * systematicScalingEtaSigmaParaPi * multiplicityCorrSigmaPi;
2344
2345
2346 Double_t sigmaRelMu = 999.;
2347 if (fTakeIntoAccountMuons) {
2348 Double_t dEdxMuExpected = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kMuon, AliTPCPIDResponse::kdEdxDefault,
2349 kTRUE, kFALSE);
2350 Double_t systematicScalingEtaSigmaParaMu = 1.;
2351 if (doSigmaSystematics) {
2352 Double_t scaleFactor = 0.5 * (1. + TMath::Erf((dEdxMuExpected - fSystematicScalingEtaSigmaParaThreshold) / 25.));
2353 systematicScalingEtaSigmaParaMu = fSystematicScalingEtaSigmaParaBelowThreshold * (1 - scaleFactor)
2354 + fSystematicScalingEtaSigmaParaAboveThreshold * scaleFactor;
2355 }
2356 sigmaRelMu = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kMuon, AliTPCPIDResponse::kdEdxDefault, kTRUE, kFALSE)
2357 / dEdxMuExpected * systematicScalingEtaSigmaParaMu * multiplicityCorrSigmaMu;
2358 }
e131b05f 2359
5709c40a 2360
2361 Double_t dEdxPrExpected = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kProton, AliTPCPIDResponse::kdEdxDefault,
2362 kTRUE, kFALSE);
2363 Double_t systematicScalingEtaSigmaParaPr = 1.;
2364 if (doSigmaSystematics) {
2365 Double_t scaleFactor = 0.5 * (1. + TMath::Erf((dEdxPrExpected - fSystematicScalingEtaSigmaParaThreshold) / 25.));
2366 systematicScalingEtaSigmaParaPr = fSystematicScalingEtaSigmaParaBelowThreshold * (1 - scaleFactor)
2367 + fSystematicScalingEtaSigmaParaAboveThreshold * scaleFactor;
2368 }
2369 Double_t sigmaRelPr = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kProton, AliTPCPIDResponse::kdEdxDefault,
2370 kTRUE, kFALSE)
2371 / dEdxPrExpected * systematicScalingEtaSigmaParaPr * multiplicityCorrSigmaPr;
e131b05f 2372
2373 // Now scale the (possibly modified) spline values with the (possibly modified) correction factors
2374 dEdxEl *= etaCorrEl * multiplicityCorrEl;
2375 dEdxKa *= etaCorrKa * multiplicityCorrKa;
2376 dEdxPi *= etaCorrPi * multiplicityCorrPi;
2377 dEdxMu *= etaCorrMu * multiplicityCorrMu;
2378 dEdxPr *= etaCorrPr * multiplicityCorrPr;
2379
2380 // Finally, get the absolute sigma
2381 sigmaEl = sigmaRelEl * dEdxEl;
2382 sigmaKa = sigmaRelKa * dEdxKa;
2383 sigmaPi = sigmaRelPi * dEdxPi;
2384 sigmaMu = sigmaRelMu * dEdxMu;
2385 sigmaPr = sigmaRelPr * dEdxPr;
2386
2387 }
2388 else {
2389 // No systematic studies on expected signal - just take it as it comve from the TPCPIDResponse
2390 dEdxEl = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kElectron, AliTPCPIDResponse::kdEdxDefault,
2391 fPIDResponse->UseTPCEtaCorrection(),
2392 fPIDResponse->UseTPCMultiplicityCorrection());
2393 dEdxKa = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kKaon, AliTPCPIDResponse::kdEdxDefault,
2394 fPIDResponse->UseTPCEtaCorrection(),
2395 fPIDResponse->UseTPCMultiplicityCorrection());
2396 dEdxPi = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kPion, AliTPCPIDResponse::kdEdxDefault,
2397 fPIDResponse->UseTPCEtaCorrection(),
2398 fPIDResponse->UseTPCMultiplicityCorrection());
2399 dEdxMu = !fTakeIntoAccountMuons ? -1 :
2400 fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kMuon, AliTPCPIDResponse::kdEdxDefault,
2401 fPIDResponse->UseTPCEtaCorrection(),
2402 fPIDResponse->UseTPCMultiplicityCorrection());
2403 dEdxPr = fPIDResponse->GetTPCResponse().GetExpectedSignal(track, AliPID::kProton, AliTPCPIDResponse::kdEdxDefault,
2404 fPIDResponse->UseTPCEtaCorrection(),
2405 fPIDResponse->UseTPCMultiplicityCorrection());
2406
2407 sigmaEl = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kElectron, AliTPCPIDResponse::kdEdxDefault,
2408 fPIDResponse->UseTPCEtaCorrection(),
2409 fPIDResponse->UseTPCMultiplicityCorrection());
2410 sigmaKa = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kKaon, AliTPCPIDResponse::kdEdxDefault,
2411 fPIDResponse->UseTPCEtaCorrection(),
2412 fPIDResponse->UseTPCMultiplicityCorrection());
2413 sigmaPi = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kPion, AliTPCPIDResponse::kdEdxDefault,
2414 fPIDResponse->UseTPCEtaCorrection(),
2415 fPIDResponse->UseTPCMultiplicityCorrection());
2416 sigmaMu = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kMuon, AliTPCPIDResponse::kdEdxDefault,
2417 fPIDResponse->UseTPCEtaCorrection(),
2418 fPIDResponse->UseTPCMultiplicityCorrection());
2419 sigmaPr = fPIDResponse->GetTPCResponse().GetExpectedSigma(track, AliPID::kProton, AliTPCPIDResponse::kdEdxDefault,
2420 fPIDResponse->UseTPCEtaCorrection(),
2421 fPIDResponse->UseTPCMultiplicityCorrection());
2422 }
e131b05f 2423
2424 Double_t deltaPrimeElectron = (dEdxEl > 0) ? dEdxTPC / dEdxEl : -1;
2425 if (dEdxEl <= 0) {
2426 Printf("Error: Expected TPC signal <= 0 for electron hypothesis");
2427 return kFALSE;
2428 }
2429
2430 Double_t deltaPrimeKaon = (dEdxKa > 0) ? dEdxTPC / dEdxKa : -1;
2431 if (dEdxKa <= 0) {
2432 Printf("Error: Expected TPC signal <= 0 for kaon hypothesis");
2433 return kFALSE;
2434 }
2435
2436 Double_t deltaPrimePion = (dEdxPi > 0) ? dEdxTPC / dEdxPi : -1;
2437 if (dEdxPi <= 0) {
2438 Printf("Error: Expected TPC signal <= 0 for pion hypothesis");
2439 return kFALSE;
2440 }
2441
2442 Double_t deltaPrimeProton = (dEdxPr > 0) ? dEdxTPC / dEdxPr : -1;
2443 if (dEdxPr <= 0) {
2444 Printf("Error: Expected TPC signal <= 0 for proton hypothesis");
2445 return kFALSE;
2446 }
e131b05f 2447
9e95a906 2448 if(fDebug > 2)
2449 printf("File: %s, Line: %d: ProcessTrack -> Compute probabilities\n", (char*)__FILE__, __LINE__);
e131b05f 2450
44ed76dd 2451
e131b05f 2452 // Use probabilities to weigh the response generation for the different species.
2453 // Also determine the most probable particle type.
2454 Double_t prob[AliPID::kSPECIESC];
2455 for (Int_t i = 0; i < AliPID::kSPECIESC; i++)
2456 prob[i] = 0;
2457
2458 fPIDcombined->ComputeProbabilities(track, fPIDResponse, prob);
2459
44ed76dd 2460 // If muons are not to be taken into account, just set their probability to zero and normalise the remaining probabilities later
e131b05f 2461 if (!fTakeIntoAccountMuons)
2462 prob[AliPID::kMuon] = 0;
2463
44ed76dd 2464 // Priors might cause problems in case of mismatches, i.e. mismatch particles will be identified as pions.
2465 // Can be easily caught for low p_TPC by just setting pion (and muon) probs to zero, if dEdx is larger than
2466 // expected dEdx of electrons and kaons
2467 if (pTPC < 1. && dEdxTPC > dEdxEl && dEdxTPC > dEdxKa) {
2468 prob[AliPID::kMuon] = 0;
2469 prob[AliPID::kPion] = 0;
2470 }
e131b05f 2471
44ed76dd 2472
2473 // Bug: One can set the number of species for PIDcombined, but PIDcombined will call PIDresponse, which writes without testing
2474 // the probs for kSPECIESC (including light nuclei) into the array.
2475 // In this case, when only kSPECIES are considered, the probabilities have to be rescaled!
2476
2477 // Exceptions:
2478 // 1. If the most probable PID is a light nucleus and above expected dEdx + 5 sigma of el to be sure not to mix up things in the
2479 // high-pT region (also contribution there completely negligable!)
2480 // 2. If dEdx is larger than the expected dEdx + 5 sigma of el and pr, it is most likely a light nucleus
2481 // (if dEdx is more than 5 sigma away from expectation, flat TPC probs are set... and the light nuclei splines are not
2482 // too accurate)
2483 // In these cases:
2484 // The highest expected dEdx is either for pr or for el. Assign 100% probability to the species with highest expected dEdx then.
2485 // In all other cases: Throw away light nuclei probs and rescale other probs to 100%.
2486
2487 // Find most probable species for the ID
2488 Int_t maxIndex = TMath::LocMax(AliPID::kSPECIESC, prob);
2489
2490 if ((prob[maxIndex] > 0 && maxIndex >= AliPID::kSPECIES && dEdxTPC > dEdxEl + 5. * sigmaEl) ||
2491 (dEdxTPC > dEdxEl + 5. * sigmaEl && dEdxTPC > dEdxPr + 5. * sigmaPr)) {
2492 for (Int_t i = 0; i < AliPID::kSPECIESC; i++)
2493 prob[i] = 0;
2494
2495 if (dEdxEl > dEdxPr) {
2496 prob[AliPID::kElectron] = 1.;
2497 maxIndex = AliPID::kElectron;
2498 }
2499 else {
2500 prob[AliPID::kProton] = 1.;
2501 maxIndex = AliPID::kProton;
2502 }
2503 }
2504 else {
2505 for (Int_t i = AliPID::kSPECIES; i < AliPID::kSPECIESC; i++)
2506 prob[i] = 0;
2507
2508 Double_t probSum = 0.;
e131b05f 2509 for (Int_t i = 0; i < AliPID::kSPECIES; i++)
44ed76dd 2510 probSum += prob[i];
2511
2512 if (probSum > 0) {
2513 for (Int_t i = 0; i < AliPID::kSPECIES; i++)
2514 prob[i] /= probSum;
2515 }
2516
2517 // If all probabilities are equal (should only happen if no priors are used), set most probable PID to pion
2518 // because LocMax returns just the first maximal value (i.e. AliPID::kElectron)
2519 if (TMath::Abs(prob[maxIndex] - 1. / AliPID::kSPECIES) < 1e-5)
2520 maxIndex = AliPID::kPion;
2521
2522 // In all other cases, the maximum remains untouched from the scaling (and is < AliPID::kSPECIES by construction)
e131b05f 2523 }
2524
2525 if (!isMC) {
44ed76dd 2526 // Clean up the most probable PID for low momenta (not an issue for the probabilities, since fractions small,
2527 // but to get clean (and nice looking) templates (if most probable PID is used instead of generated responses), it should be done).
2528 // Problem: If more than 5 sigma away (and since priors also included), most probable PID for dEdx around protons could be pions.
2529 // Idea: Only clean selection for K and p possible. So, just take for the most probable PID the probs from TPC only calculated
2530 // by hand without restriction to 5 sigma and without priors. This will not affect the template generation, but afterwards one
2531 // can replace the K and p templates with the most probable PID distributions, so all other species templates remain the same.
2532 // I.e. it doesn't hurt if the most probable PID is distributed incorrectly between el, pi, mu.
2533 Bool_t maxIndexSetManually = kFALSE;
2534
2535 if (pTPC < 1.) {
2536 Double_t probManualTPC[AliPID::kSPECIES];
2537 for (Int_t i = 0; i < AliPID::kSPECIES; i++)
2538 probManualTPC[i] = 0;
2539
2540 probManualTPC[AliPID::kElectron] = TMath::Exp(-0.5*(dEdxTPC-dEdxEl)*(dEdxTPC-dEdxEl)/(sigmaEl*sigmaEl))/sigmaEl;
2541 // Muons are not important here, just ignore and save processing time
2542 probManualTPC[AliPID::kMuon] = 0.;//TMath::Exp(-0.5*(dEdxTPC-dEdxMu)*(dEdxTPC-dEdxMu)/(sigmaMu*sigmaMu))/sigmaMu;
2543 probManualTPC[AliPID::kPion] = TMath::Exp(-0.5*(dEdxTPC-dEdxPi)*(dEdxTPC-dEdxPi)/(sigmaPi*sigmaPi))/sigmaPi;
2544 probManualTPC[AliPID::kKaon] = TMath::Exp(-0.5*(dEdxTPC-dEdxKa)*(dEdxTPC-dEdxKa)/(sigmaKa*sigmaKa))/sigmaKa;
2545 probManualTPC[AliPID::kProton] = TMath::Exp(-0.5*(dEdxTPC-dEdxPr)*(dEdxTPC-dEdxPr)/(sigmaPr*sigmaPr))/sigmaPr;
2546
2547 const Int_t maxIndexManualTPC = TMath::LocMax(AliPID::kSPECIES, probManualTPC);
2548 // Should always be > 0, but in case the dEdx is far off such that the machine accuracy sets every prob to zero,
2549 // better take the "old" result
2550 if (probManualTPC[maxIndexManualTPC] > 0.)
2551 maxIndex = maxIndexManualTPC;
2552
2553 maxIndexSetManually = kTRUE;
e131b05f 2554 }
44ed76dd 2555
2556
e131b05f 2557 // Translate from AliPID numbering to numbering of this class
44ed76dd 2558 if (prob[maxIndex] > 0 || maxIndexSetManually) {
e131b05f 2559 if (maxIndex == AliPID::kElectron)
2560 binMC = 0;
2561 else if (maxIndex == AliPID::kKaon)
2562 binMC = 1;
2563 else if (maxIndex == AliPID::kMuon)
2564 binMC = 2;
2565 else if (maxIndex == AliPID::kPion)
2566 binMC = 3;
2567 else if (maxIndex == AliPID::kProton)
2568 binMC = 4;
2569 else
2570 binMC = -1;
2571 }
2572 else {
2573 // Only take track into account for expectation values, if valid pid response is available.. Otherwise: Set to underflow bin.
2574 binMC = -1;
2575 }
2576 }
2577
2578 /*
2579 //For testing: Swap p<->pT to analyse pure p-dependence => Needs to be removed later
2580 Double_t temp = pT;
2581 pT = pTPC;
2582 pTPC = temp;
2583 */
2584
77324970 2585 TOFpidInfo tofPIDinfo = GetTOFType(track, fTOFmode);
e131b05f 2586
2587 Double_t entry[fStoreAdditionalJetInformation ? kDataNumAxes : kDataNumAxes - fgkNumJetAxes];
2588 entry[kDataMCID] = binMC;
2589 entry[kDataSelectSpecies] = 0;
2590 entry[kDataPt] = pT;
2591 entry[kDataDeltaPrimeSpecies] = deltaPrimeElectron;
2592 entry[kDataCentrality] = centralityPercentile;
2593
2594 if (fStoreAdditionalJetInformation) {
2595 entry[kDataJetPt] = jetPt;
2596 entry[kDataZ] = z;
2597 entry[kDataXi] = xi;
2598 }
2599
2600 entry[GetIndexOfChargeAxisData()] = trackCharge;
77324970 2601 entry[GetIndexOfTOFpidInfoAxisData()] = tofPIDinfo;
e131b05f 2602
2603 fhPIDdataAll->Fill(entry);
2604
2605 entry[kDataSelectSpecies] = 1;
e131b05f 2606 entry[kDataDeltaPrimeSpecies] = deltaPrimeKaon;
e131b05f 2607 fhPIDdataAll->Fill(entry);
2608
2609 entry[kDataSelectSpecies] = 2;
e131b05f 2610 entry[kDataDeltaPrimeSpecies] = deltaPrimePion;
e131b05f 2611 fhPIDdataAll->Fill(entry);
2612
2613 entry[kDataSelectSpecies] = 3;
e131b05f 2614 entry[kDataDeltaPrimeSpecies] = deltaPrimeProton;
e131b05f 2615 fhPIDdataAll->Fill(entry);
2616
2617
2618 // Construct the expected shape for the transition p -> pT
2619
2620 Double_t genEntry[fStoreAdditionalJetInformation ? kGenNumAxes : kGenNumAxes - fgkNumJetAxes];
2621 genEntry[kGenMCID] = binMC;
2622 genEntry[kGenSelectSpecies] = 0;
2623 genEntry[kGenPt] = pT;
2624 genEntry[kGenDeltaPrimeSpecies] = -999;
2625 genEntry[kGenCentrality] = centralityPercentile;
2626
2627 if (fStoreAdditionalJetInformation) {
2628 genEntry[kGenJetPt] = jetPt;
2629 genEntry[kGenZ] = z;
2630 genEntry[kGenXi] = xi;
2631 }
2632
2633 genEntry[GetIndexOfChargeAxisGen()] = trackCharge;
77324970 2634 genEntry[GetIndexOfTOFpidInfoAxisGen()] = tofPIDinfo;
e131b05f 2635
77324970
CKB		 2636 // Generate numGenEntries "responses" with fluctuations around the expected values.
2637 // fgkMaxNumGenEntries = 500 turned out to give reasonable templates even for highest track pT in 15-20 GeV/c jet pT bin.
2638 Int_t numGenEntries = fgkMaxNumGenEntries; // fgkMaxNumGenEntries = 500
e131b05f 2639
77324970
CKB		 2640 /*OLD: Different number of responses depending on pT and jet pT for fgkMaxNumGenEntries = 1000
2641 * => Problem: If threshold to higher number of responses inside a bin (or after rebinning), then the template
2642 * is biased to the higher pT.
e131b05f 2643 // Generate numGenEntries "responses" with fluctuations around the expected values.
2644 // The higher the (transverse) momentum, the more "responses" will be generated in order not to run out of statistics too fast.
2645 Int_t numGenEntries = 10;
77324970 2646
e131b05f 2647 // Jets have even worse statistics, therefore, scale numGenEntries further
2648 if (jetPt >= 40)
2649 numGenEntries *= 20;
2650 else if (jetPt >= 20)
2651 numGenEntries *= 10;
2652 else if (jetPt >= 10)
2653 numGenEntries *= 2;
2654
2655
77324970 2656
e131b05f 2657 // Do not generate more entries than available in memory!
2658 if (numGenEntries > fgkMaxNumGenEntries)// fgkMaxNumGenEntries = 1000
2659 numGenEntries = fgkMaxNumGenEntries;
77324970
CKB		 2660 */
2661
2662
e131b05f 2663 ErrorCode errCode = kNoErrors;
2664
9e95a906 2665 if(fDebug > 2)
2666 printf("File: %s, Line: %d: ProcessTrack -> Generate Responses\n", (char*)__FILE__, __LINE__);
2667
e131b05f 2668 // Electrons
2669 errCode = GenerateDetectorResponse(errCode, 1., sigmaEl / dEdxEl, fGenRespElDeltaPrimeEl, numGenEntries);
2670 errCode = GenerateDetectorResponse(errCode, dEdxEl / dEdxKa, sigmaEl / dEdxKa, fGenRespElDeltaPrimeKa, numGenEntries);
2671 errCode = GenerateDetectorResponse(errCode, dEdxEl / dEdxPi, sigmaEl / dEdxPi, fGenRespElDeltaPrimePi, numGenEntries);
2672 errCode = GenerateDetectorResponse(errCode, dEdxEl / dEdxPr, sigmaEl / dEdxPr, fGenRespElDeltaPrimePr, numGenEntries);
2673
2674 // Kaons
2675 errCode = GenerateDetectorResponse(errCode, dEdxKa / dEdxEl, sigmaKa / dEdxEl, fGenRespKaDeltaPrimeEl, numGenEntries);
2676 errCode = GenerateDetectorResponse(errCode, 1., sigmaKa / dEdxKa, fGenRespKaDeltaPrimeKa, numGenEntries);
2677 errCode = GenerateDetectorResponse(errCode, dEdxKa / dEdxPi, sigmaKa / dEdxPi, fGenRespKaDeltaPrimePi, numGenEntries);
2678 errCode = GenerateDetectorResponse(errCode, dEdxKa / dEdxPr, sigmaKa / dEdxPr, fGenRespKaDeltaPrimePr, numGenEntries);
2679
2680 // Pions
2681 errCode = GenerateDetectorResponse(errCode, dEdxPi / dEdxEl, sigmaPi / dEdxEl, fGenRespPiDeltaPrimeEl, numGenEntries);
2682 errCode = GenerateDetectorResponse(errCode, dEdxPi / dEdxKa, sigmaPi / dEdxKa, fGenRespPiDeltaPrimeKa, numGenEntries);
2683 errCode = GenerateDetectorResponse(errCode, 1., sigmaPi / dEdxPi, fGenRespPiDeltaPrimePi, numGenEntries);
2684 errCode = GenerateDetectorResponse(errCode, dEdxPi / dEdxPr, sigmaPi / dEdxPr, fGenRespPiDeltaPrimePr, numGenEntries);
2685
2686 // Muons, if desired
2687 if (fTakeIntoAccountMuons) {
2688 errCode = GenerateDetectorResponse(errCode, dEdxMu / dEdxEl, sigmaMu / dEdxEl, fGenRespMuDeltaPrimeEl, numGenEntries);
2689 errCode = GenerateDetectorResponse(errCode, dEdxMu / dEdxKa, sigmaMu / dEdxKa, fGenRespMuDeltaPrimeKa, numGenEntries);
2690 errCode = GenerateDetectorResponse(errCode, dEdxMu / dEdxPi, sigmaMu / dEdxPi, fGenRespMuDeltaPrimePi, numGenEntries);
2691 errCode = GenerateDetectorResponse(errCode, dEdxMu / dEdxPr, sigmaMu / dEdxPr, fGenRespMuDeltaPrimePr, numGenEntries);
2692 }
2693
2694 // Protons
2695 errCode = GenerateDetectorResponse(errCode, dEdxPr / dEdxEl, sigmaPr / dEdxEl, fGenRespPrDeltaPrimeEl, numGenEntries);
2696 errCode = GenerateDetectorResponse(errCode, dEdxPr / dEdxKa, sigmaPr / dEdxKa, fGenRespPrDeltaPrimeKa, numGenEntries);
2697 errCode = GenerateDetectorResponse(errCode, dEdxPr / dEdxPi, sigmaPr / dEdxPi, fGenRespPrDeltaPrimePi, numGenEntries);
2698 errCode = GenerateDetectorResponse(errCode, 1., sigmaPr / dEdxPr, fGenRespPrDeltaPrimePr, numGenEntries);
2699
e131b05f 2700 if (errCode != kNoErrors) {
77324970
CKB		 2701 if (errCode == kWarning && fDebug > 1) {
2702 Printf("Warning: Questionable detector response for track, most likely due to very low number of PID clusters! Debug output (dEdx_expected, sigma_expected):");
e131b05f 2703 }
2704 else
2705 Printf("Error: Failed to generate detector response for track - skipped! Debug output (dEdx_expected, sigma_expected):");
2706
77324970
CKB		 2707 if (fDebug > 1) {
2708 Printf("Pr: %e, %e", dEdxPr, sigmaPr);
2709 Printf("Pi: %e, %e", dEdxPi, sigmaPi);
2710 Printf("El: %e, %e", dEdxEl, sigmaEl);
2711 Printf("Mu: %e, %e", dEdxMu, sigmaMu);
2712 Printf("Ka: %e, %e", dEdxKa, sigmaKa);
2713 Printf("track: dEdx %f, pTPC %f, eta %f, ncl %d\n", track->GetTPCsignal(), track->GetTPCmomentum(), track->Eta(),
2714 track->GetTPCsignalN());
2715 }
e131b05f 2716
2717 if (errCode != kWarning) {
2718 fhSkippedTracksForSignalGeneration->Fill(track->GetTPCmomentum(), track->GetTPCsignalN());
2719 return kFALSE;// Skip generated response in case of error
2720 }
2721 }
2722
2723 for (Int_t n = 0; n < numGenEntries; n++) {
2724 if (!isMC || !fUseMCidForGeneration) {
2725 // If no MC info is available or shall not be used, use weighting with priors to generate entries for the different species
2726 Double_t rnd = fRandom->Rndm(); // Produce uniformly distributed floating point in ]0, 1]
2727
2728 // Consider generated response as originating from...
2729 if (rnd <= prob[AliPID::kElectron])
2730 genEntry[kGenMCID] = 0; // ... an electron
2731 else if (rnd <= prob[AliPID::kElectron] + prob[AliPID::kKaon])
2732 genEntry[kGenMCID] = 1; // ... a kaon
2733 else if (rnd <= prob[AliPID::kElectron] + prob[AliPID::kKaon] + prob[AliPID::kMuon])
2734 genEntry[kGenMCID] = 2; // ... a muon -> NOTE: prob[AliPID::kMuon] = 0 in case of fTakeIntoAccountMuons = kFALSE
2735 else if (rnd <= prob[AliPID::kElectron] + prob[AliPID::kKaon] + prob[AliPID::kMuon] + prob[AliPID::kPion])
2736 genEntry[kGenMCID] = 3; // ... a pion
2737 else
2738 genEntry[kGenMCID] = 4; // ... a proton
2739 }
2740
2741 // Electrons
2742 genEntry[kGenSelectSpecies] = 0;
e131b05f 2743 genEntry[kGenDeltaPrimeSpecies] = fGenRespElDeltaPrimeEl[n];
2744 fhGenEl->Fill(genEntry);
2745
2746 genEntry[kGenSelectSpecies] = 1;
e131b05f 2747 genEntry[kGenDeltaPrimeSpecies] = fGenRespElDeltaPrimeKa[n];
2748 fhGenEl->Fill(genEntry);
2749
2750 genEntry[kGenSelectSpecies] = 2;
e131b05f 2751 genEntry[kGenDeltaPrimeSpecies] = fGenRespElDeltaPrimePi[n];
2752 fhGenEl->Fill(genEntry);
2753
2754 genEntry[kGenSelectSpecies] = 3;
e131b05f 2755 genEntry[kGenDeltaPrimeSpecies] = fGenRespElDeltaPrimePr[n];
2756 fhGenEl->Fill(genEntry);
2757
2758 // Kaons
2759 genEntry[kGenSelectSpecies] = 0;
e131b05f 2760 genEntry[kGenDeltaPrimeSpecies] = fGenRespKaDeltaPrimeEl[n];
2761 fhGenKa->Fill(genEntry);
2762
2763 genEntry[kGenSelectSpecies] = 1;
e131b05f 2764 genEntry[kGenDeltaPrimeSpecies] = fGenRespKaDeltaPrimeKa[n];
2765 fhGenKa->Fill(genEntry);
2766
2767 genEntry[kGenSelectSpecies] = 2;
e131b05f 2768 genEntry[kGenDeltaPrimeSpecies] = fGenRespKaDeltaPrimePi[n];
2769 fhGenKa->Fill(genEntry);
2770
2771 genEntry[kGenSelectSpecies] = 3;
e131b05f 2772 genEntry[kGenDeltaPrimeSpecies] = fGenRespKaDeltaPrimePr[n];
2773 fhGenKa->Fill(genEntry);
2774
2775 // Pions
2776 genEntry[kGenSelectSpecies] = 0;
e131b05f 2777 genEntry[kGenDeltaPrimeSpecies] = fGenRespPiDeltaPrimeEl[n];
2778 fhGenPi->Fill(genEntry);
2779
2780 genEntry[kGenSelectSpecies] = 1;
e131b05f 2781 genEntry[kGenDeltaPrimeSpecies] = fGenRespPiDeltaPrimeKa[n];
2782 fhGenPi->Fill(genEntry);
2783
2784 genEntry[kGenSelectSpecies] = 2;
e131b05f 2785 genEntry[kGenDeltaPrimeSpecies] = fGenRespPiDeltaPrimePi[n];
2786 fhGenPi->Fill(genEntry);
2787
2788 genEntry[kGenSelectSpecies] = 3;
e131b05f 2789 genEntry[kGenDeltaPrimeSpecies] = fGenRespPiDeltaPrimePr[n];
2790 fhGenPi->Fill(genEntry);
2791
2792 if (fTakeIntoAccountMuons) {
2793 // Muons, if desired
2794 genEntry[kGenSelectSpecies] = 0;
e131b05f 2795 genEntry[kGenDeltaPrimeSpecies] = fGenRespMuDeltaPrimeEl[n];
2796 fhGenMu->Fill(genEntry);
2797
2798 genEntry[kGenSelectSpecies] = 1;
e131b05f 2799 genEntry[kGenDeltaPrimeSpecies] = fGenRespMuDeltaPrimeKa[n];
2800 fhGenMu->Fill(genEntry);
2801
2802 genEntry[kGenSelectSpecies] = 2;
e131b05f 2803 genEntry[kGenDeltaPrimeSpecies] = fGenRespMuDeltaPrimePi[n];
2804 fhGenMu->Fill(genEntry);
2805
2806 genEntry[kGenSelectSpecies] = 3;
e131b05f 2807 genEntry[kGenDeltaPrimeSpecies] = fGenRespMuDeltaPrimePr[n];
2808 fhGenMu->Fill(genEntry);
2809 }
2810
2811 // Protons
2812 genEntry[kGenSelectSpecies] = 0;
e131b05f 2813 genEntry[kGenDeltaPrimeSpecies] = fGenRespPrDeltaPrimeEl[n];
2814 fhGenPr->Fill(genEntry);
2815
2816 genEntry[kGenSelectSpecies] = 1;
e131b05f 2817 genEntry[kGenDeltaPrimeSpecies] = fGenRespPrDeltaPrimeKa[n];
2818 fhGenPr->Fill(genEntry);
2819
2820 genEntry[kGenSelectSpecies] = 2;
e131b05f 2821 genEntry[kGenDeltaPrimeSpecies] = fGenRespPrDeltaPrimePi[n];
2822 fhGenPr->Fill(genEntry);
2823
2824 genEntry[kGenSelectSpecies] = 3;
e131b05f 2825 genEntry[kGenDeltaPrimeSpecies] = fGenRespPrDeltaPrimePr[n];
2826 fhGenPr->Fill(genEntry);
2827 }
2828
9e95a906 2829 if(fDebug > 2)
2830 printf("File: %s, Line: %d: ProcessTrack -> Done\n", (char*)__FILE__, __LINE__);
2831
e131b05f 2832 return kTRUE;
2833}
2834
2835
2836//_____________________________________________________________________________
2837Bool_t AliAnalysisTaskPID::SetConvolutedGaussLambdaParameter(Double_t lambda)
2838{
2839 // Set the lambda parameter of the convolution to the desired value. Automatically
2840 // calculates the parameters for the transition (restricted) gauss -> convoluted gauss.
2841 fConvolutedGaussTransitionPars[2] = lambda;
2842
2843 // Save old parameters and settings of function to restore them later:
2844 Double_t* oldFuncParams = new Double_t[fkConvolutedGausNPar];
2845 for (Int_t i = 0; i < fkConvolutedGausNPar; i++)
2846 oldFuncParams[i] = fConvolutedGausDeltaPrime->GetParameter(i);
2847 Int_t oldFuncNpx = fConvolutedGausDeltaPrime->GetNpx();
2848 Double_t oldFuncRangeLow = 0, oldFuncRangeUp = 100;
2849 fConvolutedGausDeltaPrime->GetRange(oldFuncRangeLow, oldFuncRangeUp);
2850
2851 // Choose some sufficiently large range
2852 const Double_t rangeStart = 0.5;
2853 const Double_t rangeEnd = 2.0;
2854
2855 // To get the parameters for the transition, just choose arbitrarily input parameters for mu and sigma
2856 // (it makes sense to choose typical values). The ratio sigma_gauss / sigma_convolution is independent
2857 // of mu and as well the difference mu_gauss - mu_convolution.
2858 Double_t muInput = 1.0;
2859 Double_t sigmaInput = fgkSigmaReferenceForTransitionPars;
2860
2861
2862 // Step 1: Generate distribution with input parameters
2863 const Int_t nPar = 3;
2864 Double_t inputPar[nPar] = { muInput, sigmaInput, lambda };
2865
2866 TH1D* hInput = new TH1D("hInput", "Input distribution", 2000, rangeStart, rangeEnd);
2867
2868 fConvolutedGausDeltaPrime->SetParameters(inputPar);
2869 fConvolutedGausDeltaPrime->SetRange(rangeStart, rangeEnd);
2870 fConvolutedGausDeltaPrime->SetNpx(2000);
2871
2872 /*OLD
2873 // The resolution and mean of the AliPIDResponse are determined in finite intervals
2874 // of ncl (also second order effects due to finite dEdx and tanTheta).
2875 // Take this into account for the transition parameters via assuming an approximately flat
2876 // distritubtion in ncl in this interval.
2877 // NOTE: The ncl interval should be the same as the one used for the sigma map creation!
2878 const Int_t nclStart = 151;
2879 const Int_t nclEnd = 160;
2880 const Int_t nclSteps = (nclEnd - nclStart) + 1;
2881 for (Int_t ncl = nclStart; ncl <= nclEnd; ncl++) {
2882 // Resolution scales with 1/sqrt(ncl)
2883 fConvolutedGausDeltaPrime->SetParameter(1, inputPar[1] * sqrt(nclEnd) / sqrt(ncl));
2884 TH1* hProbDensity = fConvolutedGausDeltaPrime->GetHistogram();
2885
2886 for (Int_t i = 0; i < 50000000 / nclSteps; i++)
2887 hInput->Fill(hProbDensity->GetRandom());
2888 }
2889 */
2890
2891 TH1* hProbDensity = fConvolutedGausDeltaPrime->GetHistogram();
2892
2893 for (Int_t i = 0; i < 50000000; i++)
2894 hInput->Fill(hProbDensity->GetRandom());
2895
2896 // Step 2: Fit generated distribution with restricted gaussian
2897 Int_t maxBin = hInput->GetMaximumBin();
2898 Double_t max = hInput->GetBinContent(maxBin);
2899
2900 UChar_t usedBins = 1;
2901 if (maxBin > 1) {
2902 max += hInput->GetBinContent(maxBin - 1);
2903 usedBins++;
2904 }
2905 if (maxBin < hInput->GetNbinsX()) {
2906 max += hInput->GetBinContent(maxBin + 1);
2907 usedBins++;
2908 }
2909 max /= usedBins;
2910
2911 // NOTE: The range (<-> fraction of maximum) should be the same
2912 // as for the spline and eta maps creation
2913 const Double_t lowThreshold = hInput->GetXaxis()->GetBinLowEdge(hInput->FindFirstBinAbove(0.1 * max));
2914 const Double_t highThreshold = hInput->GetXaxis()->GetBinUpEdge(hInput->FindLastBinAbove(0.1 * max));
2915
2916 TFitResultPtr fitResGaussFirstStep = hInput->Fit("gaus", "QNRS", "", lowThreshold, highThreshold);
2917
2918 TFitResultPtr fitResGauss;
2919
2920 if ((Int_t)fitResGaussFirstStep == 0) {
2921 TF1 fGauss("fGauss", "[0]*TMath::Gaus(x, [1], [2], 1)", rangeStart, rangeEnd);
2922 fGauss.SetParameter(0, fitResGaussFirstStep->GetParams()[0]);
2923 fGauss.SetParError(0, fitResGaussFirstStep->GetErrors()[0]);
2924 fGauss.SetParameter(2, fitResGaussFirstStep->GetParams()[2]);
2925 fGauss.SetParError(2, fitResGaussFirstStep->GetErrors()[2]);
2926
2927 fGauss.FixParameter(1, fitResGaussFirstStep->GetParams()[1]);
2928 fitResGauss = hInput->Fit(&fGauss, "QNS", "s", rangeStart, rangeEnd);
2929 }
2930 else {
2931 fitResGauss = hInput->Fit("gaus", "QNRS", "same", rangeStart, rangeEnd);
2932 }
2933 //OLD TFitResultPtr fitResGauss = hInput->Fit("gaus", "QNRS", "", hInput->GetXaxis()->GetBinLowEdge(hInput->FindFirstBinAbove(0.1 * max)),
2934 // hInput->GetXaxis()->GetBinUpEdge(hInput->FindLastBinAbove(0.1 * max)));
2935
2936
2937 // Step 3: Use parameters from gaussian fit to obtain parameters for the transition "restricted gauss" -> "convoluted gauss"
2938
2939 // 3.1 The ratio sigmaInput / sigma_gaussFit ONLY depends on lambda (which is fixed per period) -> Calculate this first
2940 // for an arbitrary (here: typical) sigma. The ratio is then ~the same for ALL sigma for given lambda!
2941 if ((Int_t)fitResGauss != 0) {
2942 AliError("Not able to calculate parameters for the transition \"restricted gauss\" -> \"convoluted gauss\": Gauss Fit failed!\n");
2943 fConvolutedGausDeltaPrime->SetParameters(oldFuncParams);
2944 fConvolutedGausDeltaPrime->SetNpx(oldFuncNpx);
2945 fConvolutedGausDeltaPrime->SetRange(oldFuncRangeLow, oldFuncRangeUp);
2946
2947 delete hInput;
c4856fb1 2948 delete [] oldFuncParams;
e131b05f 2949
2950 return kFALSE;
2951 }
2952
2953 if (fitResGauss->GetParams()[2] <= 0) {
2954 AliError("Not able to calculate parameters for the transition \"restricted gauss\" -> \"convoluted gauss\": Sigma of gauss fit <= 0!\n");
2955 fConvolutedGausDeltaPrime->SetParameters(oldFuncParams);
2956 fConvolutedGausDeltaPrime->SetNpx(oldFuncNpx);
2957 fConvolutedGausDeltaPrime->SetRange(oldFuncRangeLow, oldFuncRangeUp);
2958
2959 delete hInput;
c4856fb1 2960 delete [] oldFuncParams;
e131b05f 2961
2962 return kFALSE;
2963 }
2964
2965 // sigma correction factor
2966 fConvolutedGaussTransitionPars[1] = sigmaInput / fitResGauss->GetParams()[2];
2967
2968 // 3.2 Now that sigma und lambda are determined, one can calculate mu by shifting the maximum to the desired position,
2969 // i.e. the maximum of the input distribution should coincide with that of the re-calculated distribution,
2970 // which is achieved by getting the same mu for the same sigma.
2971 // NOTE: For fixed lambda, the shift is proportional to sigma and independent of mu!
2972 // So, one can calculate the shift for an arbitrary fixed (here: typical)
2973 // sigma and then simply use this shift for any other sigma by scaling it correspondingly!!!
2974
2975 // Mu shift correction:
2976 // Shift in mu (difference between mean of gauss and mean of convolution) is proportional to sigma!
2977 // Thus, choose a reference sigma (typical -> 0.05), such that for arbitrary sigma one can simple scale
2978 // this shift correction with sigma / referenceSigma.
2979 fConvolutedGaussTransitionPars[0] = (fitResGauss->GetParams()[1] - muInput);
2980
2981
2982 /*Changed on 03.07.2013
2983
2984 // Maximum of fConvolutedGausDeltaPrime should agree with maximum of input
2985 Double_t par[nPar] = { fitResGauss->GetParams()[1], // just as a guess of the maximum position
2986 sigmaInput,
2987 lambda };
2988
2989 fConvolutedGausDeltaPrime->SetParameters(par);
2990
2991 Double_t maxXInput = fConvolutedGausDeltaPrime->GetMaximumX(TMath::Max(0.001, muInput - 3. * sigmaInput),
2992 muInput + 10. * sigmaInput,
2993 0.0001);
2994
2995 // Maximum shifts proportional to sigma and is linear in mu (~mean of gauss)
2996 // Maximum should be typically located within [gaussMean, gaussMean + 3 gaussSigma].
2997 // -> Larger search range for safety reasons (also: sigma and/or mean might be not 100% accurate).
2998 Double_t maxXconvoluted = fConvolutedGausDeltaPrime->GetMaximumX(TMath::Max(0.001,
2999 fitResGauss->GetParams()[1] - 3. * fitResGauss->GetParams()[2]),
3000 fitResGauss->GetParams()[1] + 10. * fitResGauss->GetParams()[2],
3001 0.0001);
3002 if (maxXconvoluted <= 0) {
3003 AliError("Not able to calculate parameters for the transition \"restricted gauss\" -> \"convoluted gauss\": Maximum of fConvolutedGausDeltaPrime <= 0!\n");
3004
3005 fConvolutedGausDeltaPrime->SetParameters(oldFuncParams);
3006 fConvolutedGausDeltaPrime->SetNpx(oldFuncNpx);
3007 fConvolutedGausDeltaPrime->SetRange(oldFuncRangeLow, oldFuncRangeUp);
3008
3009 delete hInput;
c4856fb1 3010 delete [] oldFuncParams;
e131b05f 3011
3012 return kFALSE;
3013 }
3014
3015 // maxX perfectly shifts as par[0] (scaled by sigma) -> Can shift maxX to input value.
3016 // Mu shift correction:
3017 fConvolutedGaussTransitionPars[0] = maxXconvoluted - maxXInput;
3018 */
3019
3020
3021
3022 fConvolutedGausDeltaPrime->SetParameters(oldFuncParams);
3023 fConvolutedGausDeltaPrime->SetNpx(oldFuncNpx);
3024 fConvolutedGausDeltaPrime->SetRange(oldFuncRangeLow, oldFuncRangeUp);
3025
3026 delete hInput;
c4856fb1 3027 delete [] oldFuncParams;
e131b05f 3028
3029 return kTRUE;
3030}
3031
3032
3033//_____________________________________________________________________________
9d7ad2e4 3034AliAnalysisTaskPID::ErrorCode AliAnalysisTaskPID::SetParamsForConvolutedGaus(Double_t gausMean, Double_t gausSigma)
e131b05f 3035{
3036 // Set parameters for convoluted gauss using parameters for a pure gaussian.
3037 // If SetConvolutedGaussLambdaParameter has not been called before to initialise the translation parameters,
3038 // some default parameters will be used and an error will show up.
3039
9e95a906 3040 if(fDebug > 1)
3041 printf("File: %s, Line: %d: SetParamsForConvolutedGaus: mean %e, sigma %e\n", (char*)__FILE__, __LINE__, gausMean, gausSigma);
3042
e131b05f 3043 if (fConvolutedGaussTransitionPars[1] < -998) {
3044 AliError("Transition parameters not initialised! Default parameters will be used. Please call SetConvolutedGaussLambdaParameter(...) before any calculations!");
3045 SetConvolutedGaussLambdaParameter(2.0);
3046 AliError(Form("Parameters set to:\n[0]: %f\n[1]: %f\n[2]: %f\n", fConvolutedGaussTransitionPars[0],
3047 fConvolutedGaussTransitionPars[1], fConvolutedGaussTransitionPars[2]));
3048 }
3049
3050 Double_t par[fkConvolutedGausNPar];
3051 par[2] = fConvolutedGaussTransitionPars[2];
3052 par[1] = fConvolutedGaussTransitionPars[1] * gausSigma;
3053 // maxX perfectly shifts as par[0] (scaled by sigma) -> Can shift maxX so that it sits at the right place.
3054 par[0] = gausMean - fConvolutedGaussTransitionPars[0] * par[1] / fgkSigmaReferenceForTransitionPars;
3055
3056 ErrorCode errCode = kNoErrors;
3057 fConvolutedGausDeltaPrime->SetParameters(par);
3058
9e95a906 3059 if(fDebug > 3)
3060 printf("File: %s, Line: %d: SetParamsForConvolutedGaus -> Parameters set to: %e, %e, %e (transition pars: %e, %e, %e, %e)\n",
3061 (char*)__FILE__, __LINE__, par[0], par[1], par[2], fConvolutedGaussTransitionPars[0], fConvolutedGaussTransitionPars[1],
3062 fConvolutedGaussTransitionPars[2], fgkSigmaReferenceForTransitionPars);
3063
e131b05f 3064 fConvolutedGausDeltaPrime->SetNpx(20); // Small value speeds up following algorithm (valid, since extrema far apart)
3065
3066 // Accuracy of 10^-5 is enough to get 0.1% precise peak for MIPS w.r.t. to dEdx = 2000 of protons
3067 // (should boost up the algorithm, because 10^-10 is the default value!)
3068 Double_t maxX= fConvolutedGausDeltaPrime->GetMaximumX(TMath::Max(0.001, gausMean - 2. * gausSigma),
3069 gausMean + 6. * gausSigma, 1.0E-5);
3070
3071 const Double_t maximum = fConvolutedGausDeltaPrime->Eval(maxX);
3072 const Double_t maximumFraction = maximum * fAccuracyNonGaussianTail;
3073
3074 // Estimate lower boundary for subsequent search:
3075 Double_t lowBoundSearchBoundLow = TMath::Max(1e-4, maxX - 5. * gausSigma);
3076 Double_t lowBoundSearchBoundUp = maxX;
3077
3078 Bool_t lowerBoundaryFixedAtZero = kFALSE;
3079
3080 while (fConvolutedGausDeltaPrime->Eval(lowBoundSearchBoundLow) >= maximumFraction) {
3081 if (lowBoundSearchBoundLow <= 0) {
3082 // This should only happen to low dEdx particles with very few clusters and therefore large sigma, such that the gauss goes below zero deltaPrime
3083 if (maximum <= 0) { // Something is weired
3084 printf("Error generating signal: maximum is <= 0!\n");
3085 return kError;
3086 }
3087 else {
3088 const Double_t valueAtZero = fConvolutedGausDeltaPrime->Eval(0);
3089 if (valueAtZero / maximum > 0.05) {
3090 // Too large fraction below zero deltaPrime. Signal generation cannot be reliable in this case
3091 printf("Error generating signal: Too large fraction below zero deltaPrime: convGauss(0) / convGauss(max) = %e / %e = %e!\n",
3092 valueAtZero, maximum, valueAtZero / maximum);
3093 return kError;
3094 }
3095 }
3096
3097 /*
3098 printf("Warning: LowBoundSearchBoundLow gets smaller zero -> Set left boundary to zero! Debug output: maximumFraction * fAccuracyNonGaussianTail = %e * %e = %e maxX %f, par[0] %f, par[1] %f, par[2] %f, gausMean %f, gausSigma %f\n",
3099 fConvolutedGausDeltaPrime->Eval(maxX), fAccuracyNonGaussianTail, maximumFraction, maxX, par[0], par[1], par[2], gausMean, gausSigma);
3100 */
3101
3102 lowerBoundaryFixedAtZero = kTRUE;
3103
3104 if (errCode != kError)
3105 errCode = kWarning;
3106
3107 break;
3108 }
3109
3110 lowBoundSearchBoundUp -= gausSigma;
3111 lowBoundSearchBoundLow -= gausSigma;
3112
3113 if (lowBoundSearchBoundLow < 0) {
3114 lowBoundSearchBoundLow = 0;
3115 lowBoundSearchBoundUp += gausSigma;
3116 }
3117 }
3118
3119 // Determine lower boundary inside estimated range. For small values of the maximum: Need more precision, since finer binning!
3120 Double_t rangeStart = lowerBoundaryFixedAtZero ? 0 :
3121 fConvolutedGausDeltaPrime->GetX(maximumFraction, lowBoundSearchBoundLow, lowBoundSearchBoundUp, (maxX < 0.4) ? 1e-5 : 0.001);
3122
3123 // .. and the same for the upper boundary
3124 Double_t rangeEnd = 0;
3125 // If distribution starts beyond upper boundary, everything ends up in the overflow bin. So, just reduce range and Npx to minimum
3126 if (rangeStart > fkDeltaPrimeUpLimit) {
3127 rangeEnd = rangeStart + 0.00001;
3128 fConvolutedGausDeltaPrime->SetRange(rangeStart,rangeEnd);
3129 fConvolutedGausDeltaPrime->SetNpx(4);
3130 }
3131 else {
3132 // Estimate upper boundary for subsequent search:
3133 Double_t upBoundSearchBoundUp = maxX + 5 * gausSigma;
3134 Double_t upBoundSearchBoundLow = maxX;
3135 while (fConvolutedGausDeltaPrime->Eval(upBoundSearchBoundUp) >= maximumFraction) {
3136 upBoundSearchBoundUp += gausSigma;
3137 upBoundSearchBoundLow += gausSigma;
3138 }
3139
3140 // For small values of the maximum: Need more precision, since finer binning!
3141 rangeEnd = fConvolutedGausDeltaPrime->GetX(maximumFraction, upBoundSearchBoundLow, upBoundSearchBoundUp, (maxX < 0.4) ? 1e-5 : 0.001);
3142
3143 fConvolutedGausDeltaPrime->SetRange(rangeStart,rangeEnd);
3144 fConvolutedGausDeltaPrime->SetNpx(fhPIDdataAll->GetAxis(kDataDeltaPrimeSpecies)->FindBin(rangeEnd)
3145 - fhPIDdataAll->GetAxis(kDataDeltaPrimeSpecies)->FindBin(rangeStart) + 1);
3146 //fConvolutedGausDeltaPrime->SetNpx((rangeEnd - rangeStart) / fDeltaPrimeBinWidth + 1);
3147 }
3148
9e95a906 3149 if(fDebug > 3)
3150 printf("File: %s, Line: %d: SetParamsForConvolutedGaus -> range %f - %f, error code %d\n", (char*)__FILE__, __LINE__,
3151 rangeStart, rangeEnd, errCode);
3152
e131b05f 3153 return errCode;
3154}
3155
3156
3157//________________________________________________________________________
3158void AliAnalysisTaskPID::SetUpGenHist(THnSparse* hist, Double_t* binsPt, Double_t* binsDeltaPrime, Double_t* binsCent, Double_t* binsJetPt) const
3159{
3160 // Sets bin limits for axes which are not standard binned and the axes titles.
3161
3162 hist->SetBinEdges(kGenPt, binsPt);
3163 hist->SetBinEdges(kGenDeltaPrimeSpecies, binsDeltaPrime);
3164 hist->SetBinEdges(kGenCentrality, binsCent);
3165
3166 if (fStoreAdditionalJetInformation)
3167 hist->SetBinEdges(kGenJetPt, binsJetPt);
3168
3169 // Set axes titles
3170 hist->GetAxis(kGenMCID)->SetTitle("MC PID");
3171 hist->GetAxis(kGenMCID)->SetBinLabel(1, "e");
3172 hist->GetAxis(kGenMCID)->SetBinLabel(2, "K");
3173 hist->GetAxis(kGenMCID)->SetBinLabel(3, "#mu");
3174 hist->GetAxis(kGenMCID)->SetBinLabel(4, "#pi");
3175 hist->GetAxis(kGenMCID)->SetBinLabel(5, "p");
3176
3177 hist->GetAxis(kGenSelectSpecies)->SetTitle("Select Species");
3178 hist->GetAxis(kGenSelectSpecies)->SetBinLabel(1, "e");
3179 hist->GetAxis(kGenSelectSpecies)->SetBinLabel(2, "K");
3180 hist->GetAxis(kGenSelectSpecies)->SetBinLabel(3, "#pi");
3181 hist->GetAxis(kGenSelectSpecies)->SetBinLabel(4, "p");
3182
5709c40a 3183 hist->GetAxis(kGenPt)->SetTitle("p_{T} (GeV/c)");
e131b05f 3184
3185 hist->GetAxis(kGenDeltaPrimeSpecies)->SetTitle("TPC #Delta'_{species} (arb. unit)");
3186
3187 hist->GetAxis(kGenCentrality)->SetTitle(Form("Centrality Percentile (%s)", fCentralityEstimator.Data()));
3188
3189 if (fStoreAdditionalJetInformation) {
5709c40a 3190 hist->GetAxis(kGenJetPt)->SetTitle("p_{T}^{jet} (GeV/c)");
e131b05f 3191
5709c40a 3192 hist->GetAxis(kGenZ)->SetTitle("z = p_{T}^{track} / p_{T}^{jet}");
e131b05f 3193
5709c40a 3194 hist->GetAxis(kGenXi)->SetTitle("#xi = ln(p_{T}^{jet} / p_{T}^{track})");
e131b05f 3195 }
3196
3197 hist->GetAxis(GetIndexOfChargeAxisGen())->SetTitle("Charge (e_{0})");
77324970
CKB		 3198
3199 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetTitle("TOF PID Info");
3200 // Offset is (TMath::Abs(kNoTOFinfo) + 1), such that bin 1 (= first label) corresponds to kNoTOFinfo (< 0)
3201 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetBinLabel(kNoTOFinfo + (TMath::Abs(kNoTOFinfo) + 1), "No TOF Info");
3202 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetBinLabel(kNoTOFpid + (TMath::Abs(kNoTOFinfo) + 1), "No TOF PID");
3203 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetBinLabel(kTOFpion + (TMath::Abs(kNoTOFinfo) + 1), "#pi");
3204 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetBinLabel(kTOFkaon + (TMath::Abs(kNoTOFinfo) + 1), "K");
3205 hist->GetAxis(GetIndexOfTOFpidInfoAxisGen())->SetBinLabel(kTOFproton + (TMath::Abs(kNoTOFinfo) + 1), "p");
e131b05f 3206}
3207
3208
3209//________________________________________________________________________
3210void AliAnalysisTaskPID::SetUpGenYieldHist(THnSparse* hist, Double_t* binsPt, Double_t* binsCent, Double_t* binsJetPt) const
3211{
3212 // Sets bin limits for axes which are not standard binned and the axes titles.
3213
3214 hist->SetBinEdges(kGenYieldPt, binsPt);
3215 hist->SetBinEdges(kGenYieldCentrality, binsCent);
3216 if (fStoreAdditionalJetInformation)
3217 hist->SetBinEdges(kGenYieldJetPt, binsJetPt);
3218
3219 for (Int_t i = 0; i < 5; i++)
3220 hist->GetAxis(kGenYieldMCID)->SetBinLabel(i + 1, AliPID::ParticleLatexName(i));
3221
3222 // Set axes titles
3223 hist->GetAxis(kGenYieldMCID)->SetTitle("MC PID");
5709c40a 3224 hist->GetAxis(kGenYieldPt)->SetTitle("p_{T}^{gen} (GeV/c)");
e131b05f 3225 hist->GetAxis(kGenYieldCentrality)->SetTitle(Form("Centrality Percentile (%s)", fCentralityEstimator.Data()));
3226
3227 if (fStoreAdditionalJetInformation) {
5709c40a 3228 hist->GetAxis(kGenYieldJetPt)->SetTitle("p_{T}^{jet, gen} (GeV/c)");
e131b05f 3229
5709c40a 3230 hist->GetAxis(kGenYieldZ)->SetTitle("z = p_{T}^{track} / p_{T}^{jet}");
e131b05f 3231
5709c40a 3232 hist->GetAxis(kGenYieldXi)->SetTitle("#xi = ln(p_{T}^{jet} / p_{T}^{track})");
e131b05f 3233 }
3234
3235 hist->GetAxis(GetIndexOfChargeAxisGenYield())->SetTitle("Charge (e_{0})");
3236}
3237
3238
3239//________________________________________________________________________
3240void AliAnalysisTaskPID::SetUpHist(THnSparse* hist, Double_t* binsPt, Double_t* binsDeltaPrime, Double_t* binsCent, Double_t* binsJetPt) const
3241{
3242 // Sets bin limits for axes which are not standard binned and the axes titles.
3243
3244 hist->SetBinEdges(kDataPt, binsPt);
3245 hist->SetBinEdges(kDataDeltaPrimeSpecies, binsDeltaPrime);
3246 hist->SetBinEdges(kDataCentrality, binsCent);
3247
3248 if (fStoreAdditionalJetInformation)
3249 hist->SetBinEdges(kDataJetPt, binsJetPt);
3250
3251 // Set axes titles
3252 hist->GetAxis(kDataMCID)->SetTitle("MC PID");
3253 hist->GetAxis(kDataMCID)->SetBinLabel(1, "e");
3254 hist->GetAxis(kDataMCID)->SetBinLabel(2, "K");
3255 hist->GetAxis(kDataMCID)->SetBinLabel(3, "#mu");
3256 hist->GetAxis(kDataMCID)->SetBinLabel(4, "#pi");
3257 hist->GetAxis(kDataMCID)->SetBinLabel(5, "p");
3258
3259 hist->GetAxis(kDataSelectSpecies)->SetTitle("Select Species");
3260 hist->GetAxis(kDataSelectSpecies)->SetBinLabel(1, "e");
3261 hist->GetAxis(kDataSelectSpecies)->SetBinLabel(2, "K");
3262 hist->GetAxis(kDataSelectSpecies)->SetBinLabel(3, "#pi");
3263 hist->GetAxis(kDataSelectSpecies)->SetBinLabel(4, "p");
3264
5709c40a 3265 hist->GetAxis(kDataPt)->SetTitle("p_{T} (GeV/c)");
e131b05f 3266
3267 hist->GetAxis(kDataDeltaPrimeSpecies)->SetTitle("TPC #Delta'_{species} (arb. unit)");
3268
3269 hist->GetAxis(kDataCentrality)->SetTitle(Form("Centrality Percentile (%s)", fCentralityEstimator.Data()));
3270
3271 if (fStoreAdditionalJetInformation) {
5709c40a 3272 hist->GetAxis(kDataJetPt)->SetTitle("p_{T}^{jet} (GeV/c)");
e131b05f 3273
5709c40a 3274 hist->GetAxis(kDataZ)->SetTitle("z = p_{T}^{track} / p_{T}^{jet}");
e131b05f 3275
5709c40a 3276 hist->GetAxis(kDataXi)->SetTitle("#xi = ln(p_{T}^{jet} / p_{T}^{track})");
e131b05f 3277 }
3278
3279 hist->GetAxis(GetIndexOfChargeAxisData())->SetTitle("Charge (e_{0})");
3280
77324970
CKB		 3281 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetTitle("TOF PID Info");
3282 // Offset is (TMath::Abs(kNoTOFinfo) + 1), such that bin 1 (= first label) corresponds to kNoTOFinfo (< 0)
3283 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetBinLabel(kNoTOFinfo + (TMath::Abs(kNoTOFinfo) + 1), "No TOF Info");
3284 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetBinLabel(kNoTOFpid + (TMath::Abs(kNoTOFinfo) + 1), "No TOF PID");
3285 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetBinLabel(kTOFpion + (TMath::Abs(kNoTOFinfo) + 1), "#pi");
3286 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetBinLabel(kTOFkaon + (TMath::Abs(kNoTOFinfo) + 1), "K");
3287 hist->GetAxis(GetIndexOfTOFpidInfoAxisData())->SetBinLabel(kTOFproton + (TMath::Abs(kNoTOFinfo) + 1), "p");
e131b05f 3288}
9e95a906 3289
3290
3291//________________________________________________________________________
e4351829 3292void AliAnalysisTaskPID::SetUpPtResHist(THnSparse* hist, Double_t* binsPt, Double_t* binsJetPt, Double_t* binsCent) const
9e95a906 3293{
3294 // Sets bin limits for axes which are not standard binned and the axes titles.
3295
3296 hist->SetBinEdges(kPtResJetPt, binsJetPt);
3297 hist->SetBinEdges(kPtResGenPt, binsPt);
3298 hist->SetBinEdges(kPtResRecPt, binsPt);
e4351829 3299 hist->SetBinEdges(kPtResCentrality, binsCent);
9e95a906 3300
3301 // Set axes titles
5709c40a 3302 hist->GetAxis(kPtResJetPt)->SetTitle("p_{T}^{jet, rec} (GeV/c)");
3303 hist->GetAxis(kPtResGenPt)->SetTitle("p_{T}^{gen} (GeV/c)");
3304 hist->GetAxis(kPtResRecPt)->SetTitle("p_{T}^{rec} (GeV/c)");
e4351829 3305
3306 hist->GetAxis(kPtResCharge)->SetTitle("Charge (e_{0})");
3307 hist->GetAxis(kPtResCentrality)->SetTitle(Form("Centrality Percentile (%s)", fCentralityEstimator.Data()));
9e95a906 3308}