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GetEvent() - do not skip the event if the current event is the same, because by defau...
[u/mrichter/AliRoot.git] / STEER / AliESDV0MI.cxx
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51ad6848 1/**************************************************************************
2 * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. *
3 * *
4 * Author: The ALICE Off-line Project. *
5 * Contributors are mentioned in the code where appropriate. *
6 * *
7 * Permission to use, copy, modify and distribute this software and its *
8 * documentation strictly for non-commercial purposes is hereby granted *
9 * without fee, provided that the above copyright notice appears in all *
10 * copies and that both the copyright notice and this permission notice *
11 * appear in the supporting documentation. The authors make no claims *
12 * about the suitability of this software for any purpose. It is *
13 * provided "as is" without express or implied warranty. *
14 **************************************************************************/
15
16/* $Id$ */
17
18//-------------------------------------------------------------------------
c1e38247 19//
20// Implementation of the ESD V0MI vertex class
21// This class is part of the Event Data Summary
22// set of classes and contains information about
23// V0 kind vertexes generated by a neutral particle
24// Numerical part - AliHelix functionality used
25//
26// Likelihoods for Point angle, DCA and Causality defined => can be used as cut parameters
27// HIGHLY recomended
28//
29// Quality information can be used as additional cut variables
30//
51ad6848 31// Origin: Marian Ivanov marian.ivanov@cern.ch
32//-------------------------------------------------------------------------
33
34#include <Riostream.h>
35#include <TMath.h>
0703142d 36
51ad6848 37#include "AliESDV0MI.h"
38#include "AliHelix.h"
39
40
41ClassImp(AliESDV0MI)
42
c1e38247 43AliESDV0MIParams AliESDV0MI::fgkParams;
44
45
90e48c0c 46AliESDV0MI::AliESDV0MI() :
47 AliESDv0(),
48 fParamP(),
49 fParamM(),
50 fID(0),
51 fDist1(-1),
52 fDist2(-1),
53 fRr(-1),
54 fStatus(0),
55 fRow0(-1),
56 fDistNorm(0),
57 fDistSigma(0),
58 fChi2Before(0),
59 fNBefore(0),
60 fChi2After(0),
61 fNAfter(0),
62 fPointAngleFi(0),
63 fPointAngleTh(0),
64 fPointAngle(0)
65{
51ad6848 66 //
67 //Dafault constructor
68 //
eaacfdf5 69 for (Int_t i=0;i<5;i++){
70 fRP[i]=fRM[i]=0;
71 }
72 fLab[0]=fLab[1]=-1;
73 fIndex[0]=fIndex[1]=-1;
6605de26 74 for (Int_t i=0;i<6;i++){fClusters[0][i]=0; fClusters[1][i]=0;}
eaacfdf5 75 fNormDCAPrim[0]=fNormDCAPrim[1]=0;
76 for (Int_t i=0;i<3;i++){fPP[i]=fPM[i]=fXr[i]=fAngle[i]=0;}
77 for (Int_t i=0;i<3;i++){fOrder[i]=0;}
78 for (Int_t i=0;i<4;i++){fCausality[i]=0;}
81e97e0d 79}
80
c1e38247 81Double_t AliESDV0MI::GetSigmaY(){
82 //
83 // return sigmay in y at vertex position using covariance matrix
84 //
85 const Double_t * cp = fParamP.GetCovariance();
86 const Double_t * cm = fParamM.GetCovariance();
c9ec41e8 87 Double_t sigmay = cp[0]+cm[0]+ cp[5]*(fParamP.GetX()-fRr)*(fParamP.GetX()-fRr)+ cm[5]*(fParamM.GetX()-fRr)*(fParamM.GetX()-fRr);
c1e38247 88 return (sigmay>0) ? TMath::Sqrt(sigmay):100;
89}
90
91Double_t AliESDV0MI::GetSigmaZ(){
92 //
93 // return sigmay in y at vertex position using covariance matrix
94 //
95 const Double_t * cp = fParamP.GetCovariance();
96 const Double_t * cm = fParamM.GetCovariance();
c9ec41e8 97 Double_t sigmaz = cp[2]+cm[2]+ cp[9]*(fParamP.GetX()-fRr)*(fParamP.GetX()-fRr)+ cm[9]*(fParamM.GetX()-fRr)*(fParamM.GetX()-fRr);
c1e38247 98 return (sigmaz>0) ? TMath::Sqrt(sigmaz):100;
99}
100
101Double_t AliESDV0MI::GetSigmaD0(){
102 //
103 // Sigma parameterization using covariance matrix
104 //
105 // sigma of distance between two tracks in vertex position
106 // sigma of DCA is proportianal to sigmaD0
107 // factor 2 difference is explained by the fact that the DCA is calculated at the position
108 // where the tracks as closest together ( not exact position of the vertex)
109 //
110 const Double_t * cp = fParamP.GetCovariance();
111 const Double_t * cm = fParamM.GetCovariance();
112 Double_t sigmaD0 = cp[0]+cm[0]+cp[2]+cm[2]+fgkParams.fPSigmaOffsetD0*fgkParams.fPSigmaOffsetD0;
c9ec41e8 113 sigmaD0 += ((fParamP.GetX()-fRr)*(fParamP.GetX()-fRr))*(cp[5]+cp[9]);
114 sigmaD0 += ((fParamM.GetX()-fRr)*(fParamM.GetX()-fRr))*(cm[5]+cm[9]);
c1e38247 115 return (sigmaD0>0)? TMath::Sqrt(sigmaD0):100;
116}
117
118
119Double_t AliESDV0MI::GetSigmaAP0(){
120 //
121 //Sigma parameterization using covariance matrices
122 //
123 Double_t prec = TMath::Sqrt((fPM[0]+fPP[0])*(fPM[0]+fPP[0])
124 +(fPM[1]+fPP[1])*(fPM[1]+fPP[1])
125 +(fPM[2]+fPP[2])*(fPM[2]+fPP[2]));
126 Double_t normp = TMath::Sqrt(fPP[0]*fPP[0]+fPP[1]*fPP[1]+fPP[2]*fPP[2])/prec; // fraction of the momenta
127 Double_t normm = TMath::Sqrt(fPM[0]*fPM[0]+fPM[1]*fPM[1]+fPM[2]*fPM[2])/prec;
128 const Double_t * cp = fParamP.GetCovariance();
129 const Double_t * cm = fParamM.GetCovariance();
130 Double_t sigmaAP0 = fgkParams.fPSigmaOffsetAP0*fgkParams.fPSigmaOffsetAP0; // minimal part
131 sigmaAP0 += (cp[5]+cp[9])*(normp*normp)+(cm[5]+cm[9])*(normm*normm); // angular resolution part
132 Double_t sigmaAP1 = GetSigmaD0()/(TMath::Abs(fRr)+0.01); // vertex position part
133 sigmaAP0 += 0.5*sigmaAP1*sigmaAP1;
134 return (sigmaAP0>0)? TMath::Sqrt(sigmaAP0):100;
135}
136
137Double_t AliESDV0MI::GetEffectiveSigmaD0(){
138 //
139 // minimax - effective Sigma parameterization
140 // p12 effective curvature and v0 radius postion used as parameters
141 //
142 Double_t p12 = TMath::Sqrt(fParamP.GetParameter()[4]*fParamP.GetParameter()[4]+
143 fParamM.GetParameter()[4]*fParamM.GetParameter()[4]);
144 Double_t sigmaED0= TMath::Max(TMath::Sqrt(fRr)-fgkParams.fPSigmaRminDE,0.0)*fgkParams.fPSigmaCoefDE*p12*p12;
145 sigmaED0*= sigmaED0;
146 sigmaED0*= sigmaED0;
147 sigmaED0 = TMath::Sqrt(sigmaED0+fgkParams.fPSigmaOffsetDE*fgkParams.fPSigmaOffsetDE);
148 return (sigmaED0<fgkParams.fPSigmaMaxDE) ? sigmaED0: fgkParams.fPSigmaMaxDE;
149}
150
151
152Double_t AliESDV0MI::GetEffectiveSigmaAP0(){
153 //
154 // effective Sigma parameterization of point angle resolution
155 //
156 Double_t p12 = TMath::Sqrt(fParamP.GetParameter()[4]*fParamP.GetParameter()[4]+
157 fParamM.GetParameter()[4]*fParamM.GetParameter()[4]);
158 Double_t sigmaAPE= fgkParams.fPSigmaBase0APE;
159 sigmaAPE+= fgkParams.fPSigmaR0APE/(fgkParams.fPSigmaR1APE+fRr);
160 sigmaAPE*= (fgkParams.fPSigmaP0APE+fgkParams.fPSigmaP1APE*p12);
161 sigmaAPE = TMath::Min(sigmaAPE,fgkParams.fPSigmaMaxAPE);
162 return sigmaAPE;
163}
164
165
166Double_t AliESDV0MI::GetMinimaxSigmaAP0(){
167 //
168 // calculate mini-max effective sigma of point angle resolution
169 //
170 //compv0->fTree->SetAlias("SigmaAP2","max(min((SigmaAP0+SigmaAPE0)*0.5,1.5*SigmaAPE0),0.5*SigmaAPE0+0.003)");
171 Double_t effectiveSigma = GetEffectiveSigmaAP0();
172 Double_t sigmaMMAP = 0.5*(GetSigmaAP0()+effectiveSigma);
173 sigmaMMAP = TMath::Min(sigmaMMAP, fgkParams.fPMaxFractionAP0*effectiveSigma);
174 sigmaMMAP = TMath::Max(sigmaMMAP, fgkParams.fPMinFractionAP0*effectiveSigma+fgkParams.fPMinAP0);
175 return sigmaMMAP;
176}
177Double_t AliESDV0MI::GetMinimaxSigmaD0(){
178 //
179 // calculate mini-max sigma of dca resolution
180 //
181 //compv0->fTree->SetAlias("SigmaD2","max(min((SigmaD0+SigmaDE0)*0.5,1.5*SigmaDE0),0.5*SigmaDE0)");
182 Double_t effectiveSigma = GetEffectiveSigmaD0();
183 Double_t sigmaMMD0 = 0.5*(GetSigmaD0()+effectiveSigma);
184 sigmaMMD0 = TMath::Min(sigmaMMD0, fgkParams.fPMaxFractionD0*effectiveSigma);
185 sigmaMMD0 = TMath::Max(sigmaMMD0, fgkParams.fPMinFractionD0*effectiveSigma+fgkParams.fPMinD0);
186 return sigmaMMD0;
187}
188
189
190Double_t AliESDV0MI::GetLikelihoodAP(Int_t mode0, Int_t mode1){
191 //
192 // get likelihood for point angle
193 //
194 Double_t sigmaAP = 0.007; //default sigma
195 switch (mode0){
196 case 0:
197 sigmaAP = GetSigmaAP0(); // mode 0 - covariance matrix estimates used
198 break;
199 case 1:
200 sigmaAP = GetEffectiveSigmaAP0(); // mode 1 - effective sigma used
201 break;
202 case 2:
203 sigmaAP = GetMinimaxSigmaAP0(); // mode 2 - minimax sigma
204 break;
205 }
206 Double_t apNorm = TMath::Min(TMath::ACos(fPointAngle)/sigmaAP,50.);
207 //normalized point angle, restricted - because of overflow problems in Exp
208 Double_t likelihood = 0;
209 switch(mode1){
210 case 0:
211 likelihood = TMath::Exp(-0.5*apNorm*apNorm);
212 // one component
213 break;
214 case 1:
215 likelihood = (TMath::Exp(-0.5*apNorm*apNorm)+0.5* TMath::Exp(-0.25*apNorm*apNorm))/1.5;
216 // two components
217 break;
218 case 2:
219 likelihood = (TMath::Exp(-0.5*apNorm*apNorm)+0.5* TMath::Exp(-0.25*apNorm*apNorm)+0.25*TMath::Exp(-0.125*apNorm*apNorm))/1.75;
220 // three components
221 break;
222 }
223 return likelihood;
224}
225
226Double_t AliESDV0MI::GetLikelihoodD(Int_t mode0, Int_t mode1){
227 //
228 // get likelihood for DCA
229 //
230 Double_t sigmaD = 0.03; //default sigma
231 switch (mode0){
232 case 0:
233 sigmaD = GetSigmaD0(); // mode 0 - covariance matrix estimates used
234 break;
235 case 1:
236 sigmaD = GetEffectiveSigmaD0(); // mode 1 - effective sigma used
237 break;
238 case 2:
239 sigmaD = GetMinimaxSigmaD0(); // mode 2 - minimax sigma
240 break;
241 }
242 Double_t dNorm = TMath::Min(fDist2/sigmaD,50.);
243 //normalized point angle, restricted - because of overflow problems in Exp
244 Double_t likelihood = 0;
245 switch(mode1){
246 case 0:
247 likelihood = TMath::Exp(-2.*dNorm);
248 // one component
249 break;
250 case 1:
251 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm))/1.5;
252 // two components
253 break;
254 case 2:
255 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm)+0.25*TMath::Exp(-0.5*dNorm))/1.75;
256 // three components
257 break;
258 }
259 return likelihood;
260
261}
262
263Double_t AliESDV0MI::GetLikelihoodC(Int_t mode0, Int_t /*mode1*/){
264 //
265 // get likelihood for Causality
266 // !!! Causality variables defined in AliITStrackerMI !!!
267 // when more information was available
268 //
269 Double_t likelihood = 0.5;
270 Double_t minCausal = TMath::Min(fCausality[0],fCausality[1]);
271 Double_t maxCausal = TMath::Max(fCausality[0],fCausality[1]);
272 // minCausal = TMath::Max(minCausal,0.5*maxCausal);
273 //compv0->fTree->SetAlias("LCausal","(1.05-(2*(0.8-exp(-max(RC.fV0rec.fCausality[0],RC.fV0rec.fCausality[1])))+2*(0.8-exp(-min(RC.fV0rec.fCausality[0],RC.fV0rec.fCausality[1]))))/2)**4");
274
275 switch(mode0){
276 case 0:
277 //normalization
278 likelihood = TMath::Power((1.05-2*(0.8-TMath::Exp(-maxCausal))),4.);
279 break;
280 case 1:
281 likelihood = TMath::Power(1.05-(2*(0.8-TMath::Exp(-maxCausal))+(2*(0.8-TMath::Exp(-minCausal))))*0.5,4.);
282 break;
283 }
284 return likelihood;
285
286}
81e97e0d 287
288void AliESDV0MI::SetCausality(Float_t pb0, Float_t pb1, Float_t pa0, Float_t pa1)
289{
290 //
291 // set probabilities
292 //
293 fCausality[0] = pb0; // probability - track 0 exist before vertex
294 fCausality[1] = pb1; // probability - track 1 exist before vertex
295 fCausality[2] = pa0; // probability - track 0 exist close after vertex
296 fCausality[3] = pa1; // probability - track 1 exist close after vertex
51ad6848 297}
6605de26 298void AliESDV0MI::SetClusters(Int_t *clp, Int_t *clm)
299{
300 //
301 // Set its clusters indexes
302 //
303 for (Int_t i=0;i<6;i++) fClusters[0][i] = clp[i];
304 for (Int_t i=0;i<6;i++) fClusters[1][i] = clm[i];
305}
306
51ad6848 307
308void AliESDV0MI::SetP(const AliExternalTrackParam & paramp) {
309 //
81e97e0d 310 // set track +
51ad6848 311 //
312 fParamP = paramp;
313}
314
315void AliESDV0MI::SetM(const AliExternalTrackParam & paramm){
316 //
81e97e0d 317 //set track -
51ad6848 318 //
319 fParamM = paramm;
51ad6848 320}
321
81e97e0d 322void AliESDV0MI::SetRp(const Double_t *rp){
323 //
324 // set pid +
325 //
326 for (Int_t i=0;i<5;i++) fRP[i]=rp[i];
327}
328
329void AliESDV0MI::SetRm(const Double_t *rm){
330 //
331 // set pid -
332 //
333 for (Int_t i=0;i<5;i++) fRM[i]=rm[i];
334}
335
336
51ad6848 337void AliESDV0MI::UpdatePID(Double_t pidp[5], Double_t pidm[5])
338{
339 //
340 // set PID hypothesy
341 //
342 // norm PID to 1
343 Float_t sump =0;
344 Float_t summ =0;
345 for (Int_t i=0;i<5;i++){
346 fRP[i]=pidp[i];
347 sump+=fRP[i];
348 fRM[i]=pidm[i];
349 summ+=fRM[i];
350 }
351 for (Int_t i=0;i<5;i++){
352 fRP[i]/=sump;
353 fRM[i]/=summ;
354 }
355}
356
357Float_t AliESDV0MI::GetProb(UInt_t p1, UInt_t p2){
358 //
359 //
360 //
361 //
362 return TMath::Max(fRP[p1]+fRM[p2], fRP[p2]+fRM[p1]);
363}
364
365Float_t AliESDV0MI::GetEffMass(UInt_t p1, UInt_t p2){
366 //
367 // calculate effective mass
368 //
0703142d 369 const Float_t kpmass[5] = {5.10000000000000037e-04,1.05660000000000004e-01,1.39570000000000000e-01,
51ad6848 370 4.93599999999999983e-01, 9.38270000000000048e-01};
371 if (p1>4) return -1;
372 if (p2>4) return -1;
0703142d 373 Float_t mass1 = kpmass[p1];
374 Float_t mass2 = kpmass[p2];
51ad6848 375 Double_t *m1 = fPP;
376 Double_t *m2 = fPM;
377 //
6605de26 378 //if (fRP[p1]+fRM[p2]<fRP[p2]+fRM[p1]){
379 // m1 = fPM;
380 // m2 = fPP;
381 //}
51ad6848 382 //
383 Float_t e1 = TMath::Sqrt(mass1*mass1+
384 m1[0]*m1[0]+
385 m1[1]*m1[1]+
386 m1[2]*m1[2]);
387 Float_t e2 = TMath::Sqrt(mass2*mass2+
388 m2[0]*m2[0]+
389 m2[1]*m2[1]+
390 m2[2]*m2[2]);
391 Float_t mass =
392 (m2[0]+m1[0])*(m2[0]+m1[0])+
393 (m2[1]+m1[1])*(m2[1]+m1[1])+
394 (m2[2]+m1[2])*(m2[2]+m1[2]);
395
396 mass = TMath::Sqrt((e1+e2)*(e1+e2)-mass);
397 return mass;
398}
399
400void AliESDV0MI::Update(Float_t vertex[3])
401{
402 //
403 // updates Kink Info
404 //
81e97e0d 405 // Float_t distance1,distance2;
406 Float_t distance2;
51ad6848 407 //
408 AliHelix phelix(fParamP);
409 AliHelix mhelix(fParamM);
410 //
411 //find intersection linear
412 //
413 Double_t phase[2][2],radius[2];
414 Int_t points = phelix.GetRPHIintersections(mhelix, phase, radius,200);
415 Double_t delta1=10000,delta2=10000;
81e97e0d 416 /*
b07a036b 417 if (points<=0) return;
51ad6848 418 if (points>0){
419 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
420 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
421 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
422 }
423 if (points==2){
424 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
425 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
426 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
427 }
428 distance1 = TMath::Min(delta1,delta2);
81e97e0d 429 */
51ad6848 430 //
431 //find intersection parabolic
432 //
433 points = phelix.GetRPHIintersections(mhelix, phase, radius);
434 delta1=10000,delta2=10000;
435 Double_t d1=1000.,d2=10000.;
29641977 436 Double_t err[3],angles[3];
b07a036b 437 if (points<=0) return;
51ad6848 438 if (points>0){
439 phelix.ParabolicDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
440 phelix.ParabolicDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
c9ec41e8 441 if (TMath::Abs(fParamP.GetX()-TMath::Sqrt(radius[0])<3) && TMath::Abs(fParamM.GetX()-TMath::Sqrt(radius[0])<3)){
29641977 442 // if we are close to vertex use error parama
443 //
444 err[1] = fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]+0.05*0.05
445 +0.3*(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
446 err[2] = fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]+0.05*0.05
447 +0.3*(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
448
449 phelix.GetAngle(phase[0][0],mhelix,phase[0][1],angles);
450 Double_t tfi = TMath::Abs(TMath::Tan(angles[0]));
451 Double_t tlam = TMath::Abs(TMath::Tan(angles[1]));
452 err[0] = err[1]/((0.2+tfi)*(0.2+tfi))+err[2]/((0.2+tlam)*(0.2+tlam));
453 err[0] = ((err[1]*err[2]/((0.2+tfi)*(0.2+tfi)*(0.2+tlam)*(0.2+tlam))))/err[0];
454 phelix.ParabolicDCA2(mhelix,phase[0][0],phase[0][1],radius[0],delta1,err);
455 }
51ad6848 456 Double_t xd[3],xm[3];
457 phelix.Evaluate(phase[0][0],xd);
458 mhelix.Evaluate(phase[0][1],xm);
459 d1 = (xd[0]-xm[0])*(xd[0]-xm[0])+(xd[1]-xm[1])*(xd[1]-xm[1])+(xd[2]-xm[2])*(xd[2]-xm[2]);
460 }
461 if (points==2){
462 phelix.ParabolicDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
463 phelix.ParabolicDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
c9ec41e8 464 if (TMath::Abs(fParamP.GetX()-TMath::Sqrt(radius[1])<3) && TMath::Abs(fParamM.GetX()-TMath::Sqrt(radius[1])<3)){
29641977 465 // if we are close to vertex use error paramatrization
466 //
467 err[1] = fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]+0.05*0.05
468 +0.3*(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
469 err[2] = fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]+0.05*0.05
470 +0.3*(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
471
472 phelix.GetAngle(phase[1][0],mhelix,phase[1][1],angles);
473 Double_t tfi = TMath::Abs(TMath::Tan(angles[0]));
474 Double_t tlam = TMath::Abs(TMath::Tan(angles[1]));
475 err[0] = err[1]/((0.2+tfi)*(0.2+tfi))+err[2]/((0.2+tlam)*(0.2+tlam));
476 err[0] = ((err[1]*err[2]/((0.2+tfi)*(0.2+tfi)*(0.2+tlam)*(0.2+tlam))))/err[0];
477 phelix.ParabolicDCA2(mhelix,phase[1][0],phase[1][1],radius[1],delta2,err);
478 }
51ad6848 479 Double_t xd[3],xm[3];
480 phelix.Evaluate(phase[1][0],xd);
481 mhelix.Evaluate(phase[1][1],xm);
482 d2 = (xd[0]-xm[0])*(xd[0]-xm[0])+(xd[1]-xm[1])*(xd[1]-xm[1])+(xd[2]-xm[2])*(xd[2]-xm[2]);
483 }
484 //
485 distance2 = TMath::Min(delta1,delta2);
486 if (delta1<delta2){
487 //get V0 info
488 Double_t xd[3],xm[3];
489 phelix.Evaluate(phase[0][0],xd);
490 mhelix.Evaluate(phase[0][1], xm);
491 fXr[0] = 0.5*(xd[0]+xm[0]);
492 fXr[1] = 0.5*(xd[1]+xm[1]);
493 fXr[2] = 0.5*(xd[2]+xm[2]);
29641977 494
495 Float_t wy = fParamP.GetCovariance()[0]/(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
496 Float_t wz = fParamP.GetCovariance()[2]/(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
497 fXr[0] = 0.5*( (1.-wy)*xd[0]+ wy*xm[0] + (1.-wz)*xd[0]+ wz*xm[0] );
498 fXr[1] = (1.-wy)*xd[1]+ wy*xm[1];
499 fXr[2] = (1.-wz)*xd[2]+ wz*xm[2];
51ad6848 500 //
501 phelix.GetMomentum(phase[0][0],fPP);
502 mhelix.GetMomentum(phase[0][1],fPM);
503 phelix.GetAngle(phase[0][0],mhelix,phase[0][1],fAngle);
504 fRr = TMath::Sqrt(fXr[0]*fXr[0]+fXr[1]*fXr[1]);
505 }
506 else{
507 Double_t xd[3],xm[3];
508 phelix.Evaluate(phase[1][0],xd);
509 mhelix.Evaluate(phase[1][1], xm);
510 fXr[0] = 0.5*(xd[0]+xm[0]);
511 fXr[1] = 0.5*(xd[1]+xm[1]);
512 fXr[2] = 0.5*(xd[2]+xm[2]);
29641977 513 Float_t wy = fParamP.GetCovariance()[0]/(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
514 Float_t wz = fParamP.GetCovariance()[2]/(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
515 fXr[0] = 0.5*( (1.-wy)*xd[0]+ wy*xm[0] + (1.-wz)*xd[0]+ wz*xm[0] );
516 fXr[1] = (1.-wy)*xd[1]+ wy*xm[1];
517 fXr[2] = (1.-wz)*xd[2]+ wz*xm[2];
51ad6848 518 //
519 phelix.GetMomentum(phase[1][0], fPP);
520 mhelix.GetMomentum(phase[1][1], fPM);
521 phelix.GetAngle(phase[1][0],mhelix,phase[1][1],fAngle);
522 fRr = TMath::Sqrt(fXr[0]*fXr[0]+fXr[1]*fXr[1]);
523 }
524 fDist1 = TMath::Sqrt(TMath::Min(d1,d2));
525 fDist2 = TMath::Sqrt(distance2);
526 //
527 //
81e97e0d 528 Double_t v[3] = {fXr[0]-vertex[0],fXr[1]-vertex[1],fXr[2]-vertex[2]};
529 Double_t p[3] = {fPP[0]+fPM[0], fPP[1]+fPM[1],fPP[2]+fPM[2]};
530 Double_t vnorm2 = v[0]*v[0]+v[1]*v[1];
c1e38247 531 if (TMath::Abs(v[2])>100000) return;
532 Double_t vnorm3 = TMath::Sqrt(TMath::Abs(v[2]*v[2]+vnorm2));
51ad6848 533 vnorm2 = TMath::Sqrt(vnorm2);
81e97e0d 534 Double_t pnorm2 = p[0]*p[0]+p[1]*p[1];
535 Double_t pnorm3 = TMath::Sqrt(p[2]*p[2]+pnorm2);
51ad6848 536 pnorm2 = TMath::Sqrt(pnorm2);
537 fPointAngleFi = (v[0]*p[0]+v[1]*p[1])/(vnorm2*pnorm2);
538 fPointAngleTh = (v[2]*p[2]+vnorm2*pnorm2)/(vnorm3*pnorm3);
539 fPointAngle = (v[0]*p[0]+v[1]*p[1]+v[2]*p[2])/(vnorm3*pnorm3);
540 //
541}
542