1 /**************************************************************************
2 * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. *
4 * Author: The ALICE Off-line Project. *
5 * Contributors are mentioned in the code where appropriate. *
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14 **************************************************************************/
18 //-------------------------------------------------------------------------
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
26 // Likelihoods for Point angle, DCA and Causality defined => can be used as cut parameters
29 // Quality information can be used as additional cut variables
31 // Origin: Marian Ivanov marian.ivanov@cern.ch
32 //-------------------------------------------------------------------------
34 #include <Riostream.h>
37 #include "AliESDV0MI.h"
43 AliESDV0MIParams AliESDV0MI::fgkParams;
46 AliESDV0MI::AliESDV0MI() :
69 for (Int_t i=0;i<4;i++){fCausality[i]=0;}
70 for (Int_t i=0;i<6;i++){fClusters[0][i]=0; fClusters[1][i]=0;}
71 for (Int_t i=0;i<2;i++){fNormDCAPrim[0]=0;fNormDCAPrim[1]=0;}
74 Double_t AliESDV0MI::GetSigmaY(){
76 // return sigmay in y at vertex position using covariance matrix
78 const Double_t * cp = fParamP.GetCovariance();
79 const Double_t * cm = fParamM.GetCovariance();
80 Double_t sigmay = cp[0]+cm[0]+ cp[5]*(fParamP.X()-fRr)*(fParamP.X()-fRr)+ cm[5]*(fParamM.X()-fRr)*(fParamM.X()-fRr);
81 return (sigmay>0) ? TMath::Sqrt(sigmay):100;
84 Double_t AliESDV0MI::GetSigmaZ(){
86 // return sigmay in y at vertex position using covariance matrix
88 const Double_t * cp = fParamP.GetCovariance();
89 const Double_t * cm = fParamM.GetCovariance();
90 Double_t sigmaz = cp[2]+cm[2]+ cp[9]*(fParamP.X()-fRr)*(fParamP.X()-fRr)+ cm[9]*(fParamM.X()-fRr)*(fParamM.X()-fRr);
91 return (sigmaz>0) ? TMath::Sqrt(sigmaz):100;
94 Double_t AliESDV0MI::GetSigmaD0(){
96 // Sigma parameterization using covariance matrix
98 // sigma of distance between two tracks in vertex position
99 // sigma of DCA is proportianal to sigmaD0
100 // factor 2 difference is explained by the fact that the DCA is calculated at the position
101 // where the tracks as closest together ( not exact position of the vertex)
103 const Double_t * cp = fParamP.GetCovariance();
104 const Double_t * cm = fParamM.GetCovariance();
105 Double_t sigmaD0 = cp[0]+cm[0]+cp[2]+cm[2]+fgkParams.fPSigmaOffsetD0*fgkParams.fPSigmaOffsetD0;
106 sigmaD0 += ((fParamP.X()-fRr)*(fParamP.X()-fRr))*(cp[5]+cp[9]);
107 sigmaD0 += ((fParamM.X()-fRr)*(fParamM.X()-fRr))*(cm[5]+cm[9]);
108 return (sigmaD0>0)? TMath::Sqrt(sigmaD0):100;
112 Double_t AliESDV0MI::GetSigmaAP0(){
114 //Sigma parameterization using covariance matrices
116 Double_t prec = TMath::Sqrt((fPM[0]+fPP[0])*(fPM[0]+fPP[0])
117 +(fPM[1]+fPP[1])*(fPM[1]+fPP[1])
118 +(fPM[2]+fPP[2])*(fPM[2]+fPP[2]));
119 Double_t normp = TMath::Sqrt(fPP[0]*fPP[0]+fPP[1]*fPP[1]+fPP[2]*fPP[2])/prec; // fraction of the momenta
120 Double_t normm = TMath::Sqrt(fPM[0]*fPM[0]+fPM[1]*fPM[1]+fPM[2]*fPM[2])/prec;
121 const Double_t * cp = fParamP.GetCovariance();
122 const Double_t * cm = fParamM.GetCovariance();
123 Double_t sigmaAP0 = fgkParams.fPSigmaOffsetAP0*fgkParams.fPSigmaOffsetAP0; // minimal part
124 sigmaAP0 += (cp[5]+cp[9])*(normp*normp)+(cm[5]+cm[9])*(normm*normm); // angular resolution part
125 Double_t sigmaAP1 = GetSigmaD0()/(TMath::Abs(fRr)+0.01); // vertex position part
126 sigmaAP0 += 0.5*sigmaAP1*sigmaAP1;
127 return (sigmaAP0>0)? TMath::Sqrt(sigmaAP0):100;
130 Double_t AliESDV0MI::GetEffectiveSigmaD0(){
132 // minimax - effective Sigma parameterization
133 // p12 effective curvature and v0 radius postion used as parameters
135 Double_t p12 = TMath::Sqrt(fParamP.GetParameter()[4]*fParamP.GetParameter()[4]+
136 fParamM.GetParameter()[4]*fParamM.GetParameter()[4]);
137 Double_t sigmaED0= TMath::Max(TMath::Sqrt(fRr)-fgkParams.fPSigmaRminDE,0.0)*fgkParams.fPSigmaCoefDE*p12*p12;
140 sigmaED0 = TMath::Sqrt(sigmaED0+fgkParams.fPSigmaOffsetDE*fgkParams.fPSigmaOffsetDE);
141 return (sigmaED0<fgkParams.fPSigmaMaxDE) ? sigmaED0: fgkParams.fPSigmaMaxDE;
145 Double_t AliESDV0MI::GetEffectiveSigmaAP0(){
147 // effective Sigma parameterization of point angle resolution
149 Double_t p12 = TMath::Sqrt(fParamP.GetParameter()[4]*fParamP.GetParameter()[4]+
150 fParamM.GetParameter()[4]*fParamM.GetParameter()[4]);
151 Double_t sigmaAPE= fgkParams.fPSigmaBase0APE;
152 sigmaAPE+= fgkParams.fPSigmaR0APE/(fgkParams.fPSigmaR1APE+fRr);
153 sigmaAPE*= (fgkParams.fPSigmaP0APE+fgkParams.fPSigmaP1APE*p12);
154 sigmaAPE = TMath::Min(sigmaAPE,fgkParams.fPSigmaMaxAPE);
159 Double_t AliESDV0MI::GetMinimaxSigmaAP0(){
161 // calculate mini-max effective sigma of point angle resolution
163 //compv0->fTree->SetAlias("SigmaAP2","max(min((SigmaAP0+SigmaAPE0)*0.5,1.5*SigmaAPE0),0.5*SigmaAPE0+0.003)");
164 Double_t effectiveSigma = GetEffectiveSigmaAP0();
165 Double_t sigmaMMAP = 0.5*(GetSigmaAP0()+effectiveSigma);
166 sigmaMMAP = TMath::Min(sigmaMMAP, fgkParams.fPMaxFractionAP0*effectiveSigma);
167 sigmaMMAP = TMath::Max(sigmaMMAP, fgkParams.fPMinFractionAP0*effectiveSigma+fgkParams.fPMinAP0);
170 Double_t AliESDV0MI::GetMinimaxSigmaD0(){
172 // calculate mini-max sigma of dca resolution
174 //compv0->fTree->SetAlias("SigmaD2","max(min((SigmaD0+SigmaDE0)*0.5,1.5*SigmaDE0),0.5*SigmaDE0)");
175 Double_t effectiveSigma = GetEffectiveSigmaD0();
176 Double_t sigmaMMD0 = 0.5*(GetSigmaD0()+effectiveSigma);
177 sigmaMMD0 = TMath::Min(sigmaMMD0, fgkParams.fPMaxFractionD0*effectiveSigma);
178 sigmaMMD0 = TMath::Max(sigmaMMD0, fgkParams.fPMinFractionD0*effectiveSigma+fgkParams.fPMinD0);
183 Double_t AliESDV0MI::GetLikelihoodAP(Int_t mode0, Int_t mode1){
185 // get likelihood for point angle
187 Double_t sigmaAP = 0.007; //default sigma
190 sigmaAP = GetSigmaAP0(); // mode 0 - covariance matrix estimates used
193 sigmaAP = GetEffectiveSigmaAP0(); // mode 1 - effective sigma used
196 sigmaAP = GetMinimaxSigmaAP0(); // mode 2 - minimax sigma
199 Double_t apNorm = TMath::Min(TMath::ACos(fPointAngle)/sigmaAP,50.);
200 //normalized point angle, restricted - because of overflow problems in Exp
201 Double_t likelihood = 0;
204 likelihood = TMath::Exp(-0.5*apNorm*apNorm);
208 likelihood = (TMath::Exp(-0.5*apNorm*apNorm)+0.5* TMath::Exp(-0.25*apNorm*apNorm))/1.5;
212 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;
219 Double_t AliESDV0MI::GetLikelihoodD(Int_t mode0, Int_t mode1){
221 // get likelihood for DCA
223 Double_t sigmaD = 0.03; //default sigma
226 sigmaD = GetSigmaD0(); // mode 0 - covariance matrix estimates used
229 sigmaD = GetEffectiveSigmaD0(); // mode 1 - effective sigma used
232 sigmaD = GetMinimaxSigmaD0(); // mode 2 - minimax sigma
235 Double_t dNorm = TMath::Min(fDist2/sigmaD,50.);
236 //normalized point angle, restricted - because of overflow problems in Exp
237 Double_t likelihood = 0;
240 likelihood = TMath::Exp(-2.*dNorm);
244 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm))/1.5;
248 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm)+0.25*TMath::Exp(-0.5*dNorm))/1.75;
256 Double_t AliESDV0MI::GetLikelihoodC(Int_t mode0, Int_t /*mode1*/){
258 // get likelihood for Causality
259 // !!! Causality variables defined in AliITStrackerMI !!!
260 // when more information was available
262 Double_t likelihood = 0.5;
263 Double_t minCausal = TMath::Min(fCausality[0],fCausality[1]);
264 Double_t maxCausal = TMath::Max(fCausality[0],fCausality[1]);
265 // minCausal = TMath::Max(minCausal,0.5*maxCausal);
266 //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");
271 likelihood = TMath::Power((1.05-2*(0.8-TMath::Exp(-maxCausal))),4.);
274 likelihood = TMath::Power(1.05-(2*(0.8-TMath::Exp(-maxCausal))+(2*(0.8-TMath::Exp(-minCausal))))*0.5,4.);
281 void AliESDV0MI::SetCausality(Float_t pb0, Float_t pb1, Float_t pa0, Float_t pa1)
286 fCausality[0] = pb0; // probability - track 0 exist before vertex
287 fCausality[1] = pb1; // probability - track 1 exist before vertex
288 fCausality[2] = pa0; // probability - track 0 exist close after vertex
289 fCausality[3] = pa1; // probability - track 1 exist close after vertex
291 void AliESDV0MI::SetClusters(Int_t *clp, Int_t *clm)
294 // Set its clusters indexes
296 for (Int_t i=0;i<6;i++) fClusters[0][i] = clp[i];
297 for (Int_t i=0;i<6;i++) fClusters[1][i] = clm[i];
301 void AliESDV0MI::SetP(const AliExternalTrackParam & paramp) {
308 void AliESDV0MI::SetM(const AliExternalTrackParam & paramm){
315 void AliESDV0MI::SetRp(const Double_t *rp){
319 for (Int_t i=0;i<5;i++) fRP[i]=rp[i];
322 void AliESDV0MI::SetRm(const Double_t *rm){
326 for (Int_t i=0;i<5;i++) fRM[i]=rm[i];
330 void AliESDV0MI::UpdatePID(Double_t pidp[5], Double_t pidm[5])
338 for (Int_t i=0;i<5;i++){
344 for (Int_t i=0;i<5;i++){
350 Float_t AliESDV0MI::GetProb(UInt_t p1, UInt_t p2){
355 return TMath::Max(fRP[p1]+fRM[p2], fRP[p2]+fRM[p1]);
358 Float_t AliESDV0MI::GetEffMass(UInt_t p1, UInt_t p2){
360 // calculate effective mass
362 const Float_t kpmass[5] = {5.10000000000000037e-04,1.05660000000000004e-01,1.39570000000000000e-01,
363 4.93599999999999983e-01, 9.38270000000000048e-01};
366 Float_t mass1 = kpmass[p1];
367 Float_t mass2 = kpmass[p2];
371 //if (fRP[p1]+fRM[p2]<fRP[p2]+fRM[p1]){
376 Float_t e1 = TMath::Sqrt(mass1*mass1+
380 Float_t e2 = TMath::Sqrt(mass2*mass2+
385 (m2[0]+m1[0])*(m2[0]+m1[0])+
386 (m2[1]+m1[1])*(m2[1]+m1[1])+
387 (m2[2]+m1[2])*(m2[2]+m1[2]);
389 mass = TMath::Sqrt((e1+e2)*(e1+e2)-mass);
393 void AliESDV0MI::Update(Float_t vertex[3])
398 // Float_t distance1,distance2;
401 AliHelix phelix(fParamP);
402 AliHelix mhelix(fParamM);
404 //find intersection linear
406 Double_t phase[2][2],radius[2];
407 Int_t points = phelix.GetRPHIintersections(mhelix, phase, radius,200);
408 Double_t delta1=10000,delta2=10000;
410 if (points<=0) return;
412 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
413 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
414 phelix.LinearDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
417 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
418 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
419 phelix.LinearDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
421 distance1 = TMath::Min(delta1,delta2);
424 //find intersection parabolic
426 points = phelix.GetRPHIintersections(mhelix, phase, radius);
427 delta1=10000,delta2=10000;
428 Double_t d1=1000.,d2=10000.;
429 Double_t err[3],angles[3];
430 if (points<=0) return;
432 phelix.ParabolicDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
433 phelix.ParabolicDCA(mhelix,phase[0][0],phase[0][1],radius[0],delta1);
434 if (TMath::Abs(fParamP.X()-TMath::Sqrt(radius[0])<3) && TMath::Abs(fParamM.X()-TMath::Sqrt(radius[0])<3)){
435 // if we are close to vertex use error parama
437 err[1] = fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]+0.05*0.05
438 +0.3*(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
439 err[2] = fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]+0.05*0.05
440 +0.3*(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
442 phelix.GetAngle(phase[0][0],mhelix,phase[0][1],angles);
443 Double_t tfi = TMath::Abs(TMath::Tan(angles[0]));
444 Double_t tlam = TMath::Abs(TMath::Tan(angles[1]));
445 err[0] = err[1]/((0.2+tfi)*(0.2+tfi))+err[2]/((0.2+tlam)*(0.2+tlam));
446 err[0] = ((err[1]*err[2]/((0.2+tfi)*(0.2+tfi)*(0.2+tlam)*(0.2+tlam))))/err[0];
447 phelix.ParabolicDCA2(mhelix,phase[0][0],phase[0][1],radius[0],delta1,err);
449 Double_t xd[3],xm[3];
450 phelix.Evaluate(phase[0][0],xd);
451 mhelix.Evaluate(phase[0][1],xm);
452 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]);
455 phelix.ParabolicDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
456 phelix.ParabolicDCA(mhelix,phase[1][0],phase[1][1],radius[1],delta2);
457 if (TMath::Abs(fParamP.X()-TMath::Sqrt(radius[1])<3) && TMath::Abs(fParamM.X()-TMath::Sqrt(radius[1])<3)){
458 // if we are close to vertex use error paramatrization
460 err[1] = fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]+0.05*0.05
461 +0.3*(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
462 err[2] = fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]+0.05*0.05
463 +0.3*(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
465 phelix.GetAngle(phase[1][0],mhelix,phase[1][1],angles);
466 Double_t tfi = TMath::Abs(TMath::Tan(angles[0]));
467 Double_t tlam = TMath::Abs(TMath::Tan(angles[1]));
468 err[0] = err[1]/((0.2+tfi)*(0.2+tfi))+err[2]/((0.2+tlam)*(0.2+tlam));
469 err[0] = ((err[1]*err[2]/((0.2+tfi)*(0.2+tfi)*(0.2+tlam)*(0.2+tlam))))/err[0];
470 phelix.ParabolicDCA2(mhelix,phase[1][0],phase[1][1],radius[1],delta2,err);
472 Double_t xd[3],xm[3];
473 phelix.Evaluate(phase[1][0],xd);
474 mhelix.Evaluate(phase[1][1],xm);
475 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]);
478 distance2 = TMath::Min(delta1,delta2);
481 Double_t xd[3],xm[3];
482 phelix.Evaluate(phase[0][0],xd);
483 mhelix.Evaluate(phase[0][1], xm);
484 fXr[0] = 0.5*(xd[0]+xm[0]);
485 fXr[1] = 0.5*(xd[1]+xm[1]);
486 fXr[2] = 0.5*(xd[2]+xm[2]);
488 Float_t wy = fParamP.GetCovariance()[0]/(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
489 Float_t wz = fParamP.GetCovariance()[2]/(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
490 fXr[0] = 0.5*( (1.-wy)*xd[0]+ wy*xm[0] + (1.-wz)*xd[0]+ wz*xm[0] );
491 fXr[1] = (1.-wy)*xd[1]+ wy*xm[1];
492 fXr[2] = (1.-wz)*xd[2]+ wz*xm[2];
494 phelix.GetMomentum(phase[0][0],fPP);
495 mhelix.GetMomentum(phase[0][1],fPM);
496 phelix.GetAngle(phase[0][0],mhelix,phase[0][1],fAngle);
497 fRr = TMath::Sqrt(fXr[0]*fXr[0]+fXr[1]*fXr[1]);
500 Double_t xd[3],xm[3];
501 phelix.Evaluate(phase[1][0],xd);
502 mhelix.Evaluate(phase[1][1], xm);
503 fXr[0] = 0.5*(xd[0]+xm[0]);
504 fXr[1] = 0.5*(xd[1]+xm[1]);
505 fXr[2] = 0.5*(xd[2]+xm[2]);
506 Float_t wy = fParamP.GetCovariance()[0]/(fParamP.GetCovariance()[0]+fParamM.GetCovariance()[0]);
507 Float_t wz = fParamP.GetCovariance()[2]/(fParamP.GetCovariance()[2]+fParamM.GetCovariance()[2]);
508 fXr[0] = 0.5*( (1.-wy)*xd[0]+ wy*xm[0] + (1.-wz)*xd[0]+ wz*xm[0] );
509 fXr[1] = (1.-wy)*xd[1]+ wy*xm[1];
510 fXr[2] = (1.-wz)*xd[2]+ wz*xm[2];
512 phelix.GetMomentum(phase[1][0], fPP);
513 mhelix.GetMomentum(phase[1][1], fPM);
514 phelix.GetAngle(phase[1][0],mhelix,phase[1][1],fAngle);
515 fRr = TMath::Sqrt(fXr[0]*fXr[0]+fXr[1]*fXr[1]);
517 fDist1 = TMath::Sqrt(TMath::Min(d1,d2));
518 fDist2 = TMath::Sqrt(distance2);
521 Double_t v[3] = {fXr[0]-vertex[0],fXr[1]-vertex[1],fXr[2]-vertex[2]};
522 Double_t p[3] = {fPP[0]+fPM[0], fPP[1]+fPM[1],fPP[2]+fPM[2]};
523 Double_t vnorm2 = v[0]*v[0]+v[1]*v[1];
524 if (TMath::Abs(v[2])>100000) return;
525 Double_t vnorm3 = TMath::Sqrt(TMath::Abs(v[2]*v[2]+vnorm2));
526 vnorm2 = TMath::Sqrt(vnorm2);
527 Double_t pnorm2 = p[0]*p[0]+p[1]*p[1];
528 Double_t pnorm3 = TMath::Sqrt(p[2]*p[2]+pnorm2);
529 pnorm2 = TMath::Sqrt(pnorm2);
530 fPointAngleFi = (v[0]*p[0]+v[1]*p[1])/(vnorm2*pnorm2);
531 fPointAngleTh = (v[2]*p[2]+vnorm2*pnorm2)/(vnorm3*pnorm3);
532 fPointAngle = (v[0]*p[0]+v[1]*p[1]+v[2]*p[2])/(vnorm3*pnorm3);