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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 //
81e97e0d 69 for (Int_t i=0;i<4;i++){fCausality[i]=0;}
6605de26 70 for (Int_t i=0;i<6;i++){fClusters[0][i]=0; fClusters[1][i]=0;}
29641977 71 for (Int_t i=0;i<2;i++){fNormDCAPrim[0]=0;fNormDCAPrim[1]=0;}
81e97e0d 72}
73
c1e38247 74Double_t AliESDV0MI::GetSigmaY(){
75 //
76 // return sigmay in y at vertex position using covariance matrix
77 //
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;
82}
83
84Double_t AliESDV0MI::GetSigmaZ(){
85 //
86 // return sigmay in y at vertex position using covariance matrix
87 //
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;
92}
93
94Double_t AliESDV0MI::GetSigmaD0(){
95 //
96 // Sigma parameterization using covariance matrix
97 //
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)
102 //
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;
109}
110
111
112Double_t AliESDV0MI::GetSigmaAP0(){
113 //
114 //Sigma parameterization using covariance matrices
115 //
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;
128}
129
130Double_t AliESDV0MI::GetEffectiveSigmaD0(){
131 //
132 // minimax - effective Sigma parameterization
133 // p12 effective curvature and v0 radius postion used as parameters
134 //
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;
138 sigmaED0*= sigmaED0;
139 sigmaED0*= sigmaED0;
140 sigmaED0 = TMath::Sqrt(sigmaED0+fgkParams.fPSigmaOffsetDE*fgkParams.fPSigmaOffsetDE);
141 return (sigmaED0<fgkParams.fPSigmaMaxDE) ? sigmaED0: fgkParams.fPSigmaMaxDE;
142}
143
144
145Double_t AliESDV0MI::GetEffectiveSigmaAP0(){
146 //
147 // effective Sigma parameterization of point angle resolution
148 //
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);
155 return sigmaAPE;
156}
157
158
159Double_t AliESDV0MI::GetMinimaxSigmaAP0(){
160 //
161 // calculate mini-max effective sigma of point angle resolution
162 //
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);
168 return sigmaMMAP;
169}
170Double_t AliESDV0MI::GetMinimaxSigmaD0(){
171 //
172 // calculate mini-max sigma of dca resolution
173 //
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);
179 return sigmaMMD0;
180}
181
182
183Double_t AliESDV0MI::GetLikelihoodAP(Int_t mode0, Int_t mode1){
184 //
185 // get likelihood for point angle
186 //
187 Double_t sigmaAP = 0.007; //default sigma
188 switch (mode0){
189 case 0:
190 sigmaAP = GetSigmaAP0(); // mode 0 - covariance matrix estimates used
191 break;
192 case 1:
193 sigmaAP = GetEffectiveSigmaAP0(); // mode 1 - effective sigma used
194 break;
195 case 2:
196 sigmaAP = GetMinimaxSigmaAP0(); // mode 2 - minimax sigma
197 break;
198 }
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;
202 switch(mode1){
203 case 0:
204 likelihood = TMath::Exp(-0.5*apNorm*apNorm);
205 // one component
206 break;
207 case 1:
208 likelihood = (TMath::Exp(-0.5*apNorm*apNorm)+0.5* TMath::Exp(-0.25*apNorm*apNorm))/1.5;
209 // two components
210 break;
211 case 2:
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;
213 // three components
214 break;
215 }
216 return likelihood;
217}
218
219Double_t AliESDV0MI::GetLikelihoodD(Int_t mode0, Int_t mode1){
220 //
221 // get likelihood for DCA
222 //
223 Double_t sigmaD = 0.03; //default sigma
224 switch (mode0){
225 case 0:
226 sigmaD = GetSigmaD0(); // mode 0 - covariance matrix estimates used
227 break;
228 case 1:
229 sigmaD = GetEffectiveSigmaD0(); // mode 1 - effective sigma used
230 break;
231 case 2:
232 sigmaD = GetMinimaxSigmaD0(); // mode 2 - minimax sigma
233 break;
234 }
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;
238 switch(mode1){
239 case 0:
240 likelihood = TMath::Exp(-2.*dNorm);
241 // one component
242 break;
243 case 1:
244 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm))/1.5;
245 // two components
246 break;
247 case 2:
248 likelihood = (TMath::Exp(-2.*dNorm)+0.5* TMath::Exp(-dNorm)+0.25*TMath::Exp(-0.5*dNorm))/1.75;
249 // three components
250 break;
251 }
252 return likelihood;
253
254}
255
256Double_t AliESDV0MI::GetLikelihoodC(Int_t mode0, Int_t /*mode1*/){
257 //
258 // get likelihood for Causality
259 // !!! Causality variables defined in AliITStrackerMI !!!
260 // when more information was available
261 //
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");
267
268 switch(mode0){
269 case 0:
270 //normalization
271 likelihood = TMath::Power((1.05-2*(0.8-TMath::Exp(-maxCausal))),4.);
272 break;
273 case 1:
274 likelihood = TMath::Power(1.05-(2*(0.8-TMath::Exp(-maxCausal))+(2*(0.8-TMath::Exp(-minCausal))))*0.5,4.);
275 break;
276 }
277 return likelihood;
278
279}
81e97e0d 280
281void AliESDV0MI::SetCausality(Float_t pb0, Float_t pb1, Float_t pa0, Float_t pa1)
282{
283 //
284 // set probabilities
285 //
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
51ad6848 290}
6605de26 291void AliESDV0MI::SetClusters(Int_t *clp, Int_t *clm)
292{
293 //
294 // Set its clusters indexes
295 //
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];
298}
299
51ad6848 300
301void AliESDV0MI::SetP(const AliExternalTrackParam & paramp) {
302 //
81e97e0d 303 // set track +
51ad6848 304 //
305 fParamP = paramp;
306}
307
308void AliESDV0MI::SetM(const AliExternalTrackParam & paramm){
309 //
81e97e0d 310 //set track -
51ad6848 311 //
312 fParamM = paramm;
51ad6848 313}
314
81e97e0d 315void AliESDV0MI::SetRp(const Double_t *rp){
316 //
317 // set pid +
318 //
319 for (Int_t i=0;i<5;i++) fRP[i]=rp[i];
320}
321
322void AliESDV0MI::SetRm(const Double_t *rm){
323 //
324 // set pid -
325 //
326 for (Int_t i=0;i<5;i++) fRM[i]=rm[i];
327}
328
329
51ad6848 330void AliESDV0MI::UpdatePID(Double_t pidp[5], Double_t pidm[5])
331{
332 //
333 // set PID hypothesy
334 //
335 // norm PID to 1
336 Float_t sump =0;
337 Float_t summ =0;
338 for (Int_t i=0;i<5;i++){
339 fRP[i]=pidp[i];
340 sump+=fRP[i];
341 fRM[i]=pidm[i];
342 summ+=fRM[i];
343 }
344 for (Int_t i=0;i<5;i++){
345 fRP[i]/=sump;
346 fRM[i]/=summ;
347 }
348}
349
350Float_t AliESDV0MI::GetProb(UInt_t p1, UInt_t p2){
351 //
352 //
353 //
354 //
355 return TMath::Max(fRP[p1]+fRM[p2], fRP[p2]+fRM[p1]);
356}
357
358Float_t AliESDV0MI::GetEffMass(UInt_t p1, UInt_t p2){
359 //
360 // calculate effective mass
361 //
0703142d 362 const Float_t kpmass[5] = {5.10000000000000037e-04,1.05660000000000004e-01,1.39570000000000000e-01,
51ad6848 363 4.93599999999999983e-01, 9.38270000000000048e-01};
364 if (p1>4) return -1;
365 if (p2>4) return -1;
0703142d 366 Float_t mass1 = kpmass[p1];
367 Float_t mass2 = kpmass[p2];
51ad6848 368 Double_t *m1 = fPP;
369 Double_t *m2 = fPM;
370 //
6605de26 371 //if (fRP[p1]+fRM[p2]<fRP[p2]+fRM[p1]){
372 // m1 = fPM;
373 // m2 = fPP;
374 //}
51ad6848 375 //
376 Float_t e1 = TMath::Sqrt(mass1*mass1+
377 m1[0]*m1[0]+
378 m1[1]*m1[1]+
379 m1[2]*m1[2]);
380 Float_t e2 = TMath::Sqrt(mass2*mass2+
381 m2[0]*m2[0]+
382 m2[1]*m2[1]+
383 m2[2]*m2[2]);
384 Float_t mass =
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]);
388
389 mass = TMath::Sqrt((e1+e2)*(e1+e2)-mass);
390 return mass;
391}
392
393void AliESDV0MI::Update(Float_t vertex[3])
394{
395 //
396 // updates Kink Info
397 //
81e97e0d 398 // Float_t distance1,distance2;
399 Float_t distance2;
51ad6848 400 //
401 AliHelix phelix(fParamP);
402 AliHelix mhelix(fParamM);
403 //
404 //find intersection linear
405 //
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;
81e97e0d 409 /*
b07a036b 410 if (points<=0) return;
51ad6848 411 if (points>0){
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);
415 }
416 if (points==2){
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);
420 }
421 distance1 = TMath::Min(delta1,delta2);
81e97e0d 422 */
51ad6848 423 //
424 //find intersection parabolic
425 //
426 points = phelix.GetRPHIintersections(mhelix, phase, radius);
427 delta1=10000,delta2=10000;
428 Double_t d1=1000.,d2=10000.;
29641977 429 Double_t err[3],angles[3];
b07a036b 430 if (points<=0) return;
51ad6848 431 if (points>0){
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);
29641977 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
436 //
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]);
441
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);
448 }
51ad6848 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]);
453 }
454 if (points==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);
29641977 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
459 //
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]);
464
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);
471 }
51ad6848 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]);
476 }
477 //
478 distance2 = TMath::Min(delta1,delta2);
479 if (delta1<delta2){
480 //get V0 info
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]);
29641977 487
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];
51ad6848 493 //
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]);
498 }
499 else{
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]);
29641977 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];
51ad6848 511 //
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]);
516 }
517 fDist1 = TMath::Sqrt(TMath::Min(d1,d2));
518 fDist2 = TMath::Sqrt(distance2);
519 //
520 //
81e97e0d 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];
c1e38247 524 if (TMath::Abs(v[2])>100000) return;
525 Double_t vnorm3 = TMath::Sqrt(TMath::Abs(v[2]*v[2]+vnorm2));
51ad6848 526 vnorm2 = TMath::Sqrt(vnorm2);
81e97e0d 527 Double_t pnorm2 = p[0]*p[0]+p[1]*p[1];
528 Double_t pnorm3 = TMath::Sqrt(p[2]*p[2]+pnorm2);
51ad6848 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);
533 //
534}
535