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