7c679be0 |
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 | //----------------------------------------------------------------------------------- |
17 | // Jet finder based on Deterministic Annealing |
18 | // For further informations about the DA working features see: |
19 | // Phys.Lett. B601 (2004) 56-63 (http://arxiv.org/abs/hep-ph/0407214) |
20 | // Author: Davide Perrino (davide.perrino@ba.infn.it, davide.perrino@cern.ch) |
21 | //----------------------------------------------------------------------------------- |
22 | |
23 | #include <TMath.h> |
24 | #include <TRandom.h> |
25 | #include <TClonesArray.h> |
26 | #include "AliJetReader.h" |
27 | #include "AliDAJetHeader.h" |
28 | #include "AliDAJetFinder.h" |
29 | |
30 | |
31 | ClassImp(AliDAJetFinder) |
32 | |
33 | |
34 | //----------------------------------------------------------------------------------- |
35 | AliDAJetFinder::AliDAJetFinder(): |
36 | fAlpha(1.01), |
37 | fDelta(1e-8), |
38 | fAvDist(1e-6), |
39 | fEps(1e-4), |
40 | fEpsMax(1e-2), |
41 | fNloopMax(100), |
42 | fBeta(0.1), |
43 | fNclustMax(0), |
44 | fPyx(0x0), |
45 | fY(0x0), |
46 | fPx(0x0), |
47 | fPy(0x0), |
48 | fXEta(0x0), |
49 | fXPhi(0x0), |
50 | fNin(0), |
51 | fHeader(0x0) |
52 | { |
53 | // Constructor |
54 | } |
55 | |
56 | //----------------------------------------------------------------------------------- |
57 | AliDAJetFinder::~AliDAJetFinder() |
58 | { |
59 | // Destructor |
60 | delete fHeader; |
61 | delete fPyx; |
62 | delete fY; |
63 | delete fPx; |
64 | delete fPy; |
65 | delete [] fXEta; |
66 | delete [] fXPhi; |
67 | } |
68 | |
69 | //----------------------------------------------------------------------------------- |
70 | void AliDAJetFinder::FindJets() |
71 | { |
72 | // Find the jets in current event |
73 | // |
74 | Float_t betaStop=100.; |
75 | |
76 | Double_t dEtSum=0; |
77 | InitDetAnn(dEtSum); |
78 | if (!fNin) return; |
79 | |
80 | Int_t nc=1,nk; |
81 | DoubleClusters(nc,nk); |
82 | do{ //loop over beta |
83 | fBeta*=fAlpha; |
84 | Annealing(nk); |
85 | NumCl(nc,nk); |
86 | }while((fBeta<betaStop || nc<4) && nc<fNclustMax); |
87 | |
88 | Int_t *xx=new Int_t[fNin]; |
89 | EndDetAnn(nk,xx,dEtSum); |
90 | StoreJets(nk,xx); |
91 | delete [] xx; |
92 | |
93 | } |
94 | |
95 | //----------------------------------------------------------------------------------- |
96 | void AliDAJetFinder::InitDetAnn(Double_t &dEtSum) |
97 | { |
98 | //Initialise the variables used by the algorithm |
99 | fBeta=0.1; |
100 | TClonesArray *lvArray = fReader->GetMomentumArray(); |
101 | fNin = lvArray->GetEntries(); |
102 | fNclustMax= fHeader->GetFixedCl() ? fHeader->GetNclustMax() : TMath::Max((Int_t)TMath::Sqrt(fNin),5); |
103 | fXEta=new Double_t[fNin]; fXPhi=new Double_t[fNin]; |
104 | fPx = new TVectorD(fNin); |
105 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
106 | TLorentzVector *lv=(TLorentzVector*)lvArray->At(iIn); |
107 | fXEta[iIn] = lv->Eta(); |
108 | fXPhi[iIn] = lv->Phi()<0 ? lv->Phi() + 2*TMath::Pi() : lv->Phi(); |
109 | (*fPx)(iIn)=lv->Pt(); |
110 | dEtSum+=(*fPx)(iIn); |
111 | } |
112 | for (Int_t iIn=0; iIn<fNin; iIn++) (*fPx)(iIn)=(*fPx)(iIn)/dEtSum; |
113 | |
114 | Int_t njdim=2*fNclustMax+1; |
115 | fPyx = new TMatrixD(fNin,njdim); |
116 | fY = new TMatrixD(4,njdim); |
117 | fPy= new TVectorD(njdim); |
118 | fY->Zero();fPyx->Zero();fPy->Zero(); |
119 | (*fPy)(0)=1; |
120 | TMatrixDColumn(*fPyx,0)=1; |
121 | Double_t ypos=0,xpos=0; |
122 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
123 | (*fY)(0,0)+=(*fPx)(iIn)*fXEta[iIn]; |
124 | ypos+=(*fPx)(iIn)*TMath::Sin(fXPhi[iIn]); |
125 | xpos+=(*fPx)(iIn)*TMath::Cos(fXPhi[iIn]); |
126 | } |
127 | (*fY)(1,0)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi(); |
128 | lvArray->Delete(); |
129 | } |
130 | |
131 | //----------------------------------------------------------------------------------- |
132 | void AliDAJetFinder::DoubleClusters(Int_t nc,Int_t &nk) |
133 | { |
134 | for(Int_t iClust=0; iClust<nc; iClust++){ |
135 | (*fPy)(iClust)=(*fPy)(iClust)/2; |
136 | (*fPy)(nc+iClust)=(*fPy)(iClust); |
137 | for(Int_t iComp=0; iComp<3; iComp++) (*fY)(iComp,nc+iClust)=(*fY)(iComp,iClust); |
138 | } |
139 | nk=2*nc; |
140 | } |
141 | |
142 | //----------------------------------------------------------------------------------- |
143 | void AliDAJetFinder::Annealing(Int_t nk) |
144 | { |
145 | // Main part of the algorithm |
146 | const Double_t pi=TMath::Pi(); |
147 | TVectorD *py = new TVectorD(nk); |
148 | TVectorD *p = new TVectorD(nk); |
149 | TMatrixD *y = new TMatrixD(4,nk); |
150 | TMatrixD *y1 = new TMatrixD(4,nk); |
151 | TMatrixD *ry = new TMatrixD(2,nk); |
152 | Double_t Dist(TVectorD,TVectorD); |
153 | |
154 | Double_t df[2]={fHeader->GetEtaCut(),pi}; |
155 | TVectorD vPart(2); |
156 | Double_t *m = new Double_t[nk]; |
157 | Double_t chi,chi1; |
158 | do{ |
159 | Int_t nloop=0; |
160 | for (Int_t iClust=0; iClust<nk; iClust++){ |
161 | for (Int_t i=0; i<3; i++)(*y1)(i,iClust)=(*fY)(i,iClust); |
162 | (*py)(iClust)=(*fPy)(iClust); |
163 | } |
164 | //perturbation of codevectors |
165 | Double_t seed=1000000*gRandom->Rndm(24); |
166 | ry->Randomize(-0.5,0.5,seed); |
167 | for (Int_t i=0; i<2; i++){ |
168 | for (Int_t iClust=0; iClust<nk/2; iClust++) |
169 | (*y1)(i,iClust)+=((*ry)(i,iClust)+TMath::Sign(0.5,(*ry)(i,iClust)))*fDelta*df[i]; |
170 | for (Int_t iClust=nk/2; iClust<nk; iClust++) |
171 | (*y1)(i,iClust)-=((*ry)(i,iClust-nk/2)+TMath::Sign(0.5,(*ry)(i,iClust-nk/2)))*fDelta*df[i]; |
172 | } |
173 | do{ |
174 | //recalculate conditional probabilities |
175 | nloop++; |
176 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
177 | vPart(0)=fXEta[iIn]; vPart(1)=fXPhi[iIn]; |
178 | for(Int_t iClust=0; iClust<nk; iClust++){ |
179 | (*fPyx)(iIn,iClust)=-log((*py)(iClust))+fBeta*Dist(vPart,TMatrixDColumn(*y1,iClust)); |
180 | m[iClust]=(*fPyx)(iIn,iClust); |
181 | } |
182 | Double_t pyxNorm=0; |
183 | Double_t minPyx=TMath::MinElement(nk,m); |
184 | for (Int_t iClust=0; iClust<nk; iClust++){ |
185 | (*fPyx)(iIn,iClust)-=minPyx; |
186 | (*fPyx)(iIn,iClust)=exp(-(*fPyx)(iIn,iClust)); |
187 | pyxNorm+=(*fPyx)(iIn,iClust); |
188 | } |
189 | for (Int_t iClust=0; iClust<nk; iClust++) (*fPyx)(iIn,iClust)/=pyxNorm; |
190 | } |
191 | p->Zero(); |
192 | y->Zero(); |
193 | //recalculate codevectors |
194 | for (Int_t iClust=0; iClust<nk; iClust++){ |
195 | Double_t xpos=0,ypos=0,pxy; |
196 | for (Int_t iIn=0; iIn<fNin; iIn++) (*p)(iClust)+=(*fPx)(iIn)*(*fPyx)(iIn,iClust); |
197 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
198 | pxy=(*fPx)(iIn)*(*fPyx)(iIn,iClust)/(*p)(iClust); |
199 | ypos+=pxy*TMath::Sin(fXPhi[iIn]); |
200 | xpos+=pxy*TMath::Cos(fXPhi[iIn]); |
201 | (*y)(0,iClust)+=pxy*fXEta[iIn]; |
202 | } |
203 | (*y)(1,iClust)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*pi; |
204 | } |
205 | //verify codevectors' stability |
206 | chi=0; |
207 | for (Int_t iClust=0; iClust<nk; iClust++){ |
208 | chi1=TMath::CosH((*y1)(0,iClust)-(*y)(0,iClust))-TMath::Cos((*y1)(1,iClust)-(*y)(1,iClust)); |
209 | chi1/=(2*TMath::CosH((*y1)(0,iClust))*TMath::CosH((*y)(0,iClust))); |
210 | chi1*=chi1; |
211 | if (chi1>chi) chi=chi1; |
212 | } |
213 | chi=TMath::Sqrt(chi); |
214 | for (Int_t iClust=0; iClust<nk; iClust++){ |
215 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)=(*y)(i,iClust); |
216 | (*py)(iClust)=(*p)(iClust); |
217 | } |
218 | if (nloop>fNloopMax){ |
219 | if (chi<fEpsMax || nloop>500) break; |
220 | } |
221 | }while (chi>fEps); |
222 | }while (chi>fEpsMax); |
223 | for (Int_t iClust=0; iClust<nk; iClust++){ //set codevectors and probability equal to those calculated |
224 | for (Int_t i=0; i<2; i++) (*fY)(i,iClust)=(*y)(i,iClust); |
225 | (*fPy)(iClust)=(*p)(iClust); |
226 | } |
227 | delete py; |
228 | delete p; |
229 | delete y; |
230 | delete y1; |
231 | delete ry; |
232 | delete [] m; |
233 | } |
234 | |
235 | //----------------------------------------------------------------------------------- |
236 | void AliDAJetFinder::NumCl(Int_t &nc,Int_t &nk) |
237 | { |
238 | static Bool_t growcl=true; |
239 | |
240 | if (nk==2) growcl=true; |
241 | if (growcl){ |
242 | //verify if two codevectors are equal within fAvDist |
243 | Int_t *nSame = new Int_t[nk]; |
244 | Int_t **iSame = new Int_t*[nk]; |
245 | Int_t **cont = new Int_t*[nk]; |
246 | for (Int_t iClust=0; iClust<nk; iClust++) cont[iClust]=new Int_t[nk],iSame[iClust]=new Int_t[nk]; |
247 | for (Int_t iClust=0; iClust<nk; iClust++){ |
248 | iSame[iClust][iClust]=1; |
249 | for (Int_t iClust1=iClust+1; iClust1<nk; iClust1++){ |
250 | Double_t eta = (*fY)(0,iClust) ; Double_t phi = (*fY)(1,iClust); |
251 | Double_t eta1 = (*fY)(0,iClust1); Double_t phi1 = (*fY)(1,iClust1); |
252 | Double_t distCl=(TMath::CosH(eta-eta1)-TMath::Cos(phi-phi1))/(2*TMath::CosH(eta)*TMath::CosH(eta1)); |
253 | if (distCl < fAvDist) iSame[iClust][iClust1]=iSame[iClust1][iClust]=1; |
254 | } |
255 | } |
256 | ReduceClusters(iSame,nk,nc,cont,nSame); |
257 | if (nc >= fNclustMax) growcl=false; |
258 | //recalculate the nc distinct codevectors |
259 | TMatrixD *pyx = new TMatrixD(fNin,2*nc); |
260 | TVectorD *py = new TVectorD(nk); |
261 | TMatrixD *y1 = new TMatrixD(3,nk); |
262 | for (Int_t iClust=0; iClust<nc; iClust++){ |
263 | for(Int_t j=0; j<nSame[iClust]; j++){ |
264 | Int_t iClust1 = cont[iClust][j]; |
265 | for (Int_t iIn=0; iIn<fNin; iIn++) (*pyx)(iIn,iClust)+=(*fPyx)(iIn,iClust1); |
266 | (*py)(iClust)+=(*fPy)(iClust1); |
267 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)+=(*fY)(i,iClust1); |
268 | } |
269 | for (Int_t i=0; i<2; i++) (*y1)(i,iClust)/=nSame[iClust]; |
270 | } |
271 | if (nc > nk/2){ |
272 | for (Int_t iClust=0; iClust<nc; iClust++){ |
273 | for (Int_t iIn=0; iIn<fNin; iIn++) (*fPyx)(iIn,iClust)=(*pyx)(iIn,iClust); |
274 | for (Int_t iComp=0; iComp<2; iComp++) (*fY)(iComp,iClust)=(*y1)(iComp,iClust); |
275 | (*fPy)(iClust)=(*py)(iClust); |
276 | } |
277 | nk=nc; |
278 | if (growcl) DoubleClusters(nc,nk); |
279 | } |
280 | delete [] nSame; |
281 | delete [] iSame; |
282 | delete [] cont; |
283 | delete pyx; |
284 | delete py; |
285 | delete y1; |
286 | } |
287 | |
288 | } |
289 | |
290 | //----------------------------------------------------------------------------------- |
291 | void AliDAJetFinder::ReduceClusters(Int_t **iSame,Int_t nc,Int_t &ncout,Int_t **cont,Int_t *nSameOut) |
292 | { |
293 | Int_t *nSame = new Int_t[nc]; |
294 | Int_t *iperm = new Int_t[nc]; |
295 | Int_t *go = new Int_t[nc]; |
296 | for (Int_t iCl=0; iCl<nc; iCl++){ |
297 | nSame[iCl]=0; |
298 | for (Int_t jCl=0; jCl<nc; jCl++) nSame[iCl]+=iSame[iCl][jCl]; |
299 | iperm[iCl]=iCl; |
300 | go[iCl]=1; |
301 | } |
302 | TMath::Sort(nc,nSame,iperm,true); |
303 | Int_t l=0; |
304 | for (Int_t iCl=0; iCl<nc; iCl++){ |
305 | Int_t k=iperm[iCl]; |
306 | if (go[k] == 1){ |
307 | Int_t m=0; |
308 | for (Int_t jCl=0; jCl<nc; jCl++){ |
309 | if (iSame[k][jCl] == 1){ |
310 | cont[l][m]=jCl; |
311 | go[jCl]=0; |
312 | m++; |
313 | } |
314 | } |
315 | nSameOut[l]=m; |
316 | l++; |
317 | } |
318 | } |
319 | ncout=l; |
320 | delete [] nSame; |
321 | delete [] iperm; |
322 | delete [] go; |
323 | } |
324 | |
325 | //----------------------------------------------------------------------------------- |
326 | void AliDAJetFinder::EndDetAnn(Int_t &nk,Int_t *xx,Double_t etx) |
327 | { |
328 | //now assign each particle to only one cluster |
329 | Double_t *clusters=new Double_t[nk]; |
330 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
331 | for (Int_t iClust=0; iClust<nk; iClust++) clusters[iClust]=(*fPyx)(iIn,iClust); |
332 | xx[iIn]=TMath::LocMax(nk,clusters); |
333 | } |
334 | delete [] clusters; |
335 | |
336 | //recalculate codevectors, having all p(y|x)=0 or 1 |
337 | fY->Zero(); |
338 | fPyx->Zero(); |
339 | fPy->Zero(); |
340 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
341 | Int_t iClust=xx[iIn]; |
342 | (*fPyx)(iIn,iClust)=1; |
343 | (*fPy)(iClust)+=(*fPx)(iIn); |
344 | (*fY)(0,iClust)+=(*fPx)(iIn)*fXEta[iIn]; |
345 | (*fY)(3,iClust)+=(*fPx)(iIn)*etx; |
346 | } |
347 | Int_t k=0; |
348 | for (Int_t iClust=0; iClust<nk; iClust++){ |
349 | if ((*fPy)(iClust)>0){ |
350 | Double_t xpos=0,ypos=0,pxy; |
351 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
352 | pxy=(*fPx)(iIn)*(*fPyx)(iIn,iClust)/(*fPy)(iClust); |
353 | ypos+=pxy*TMath::Sin(fXPhi[iIn]); |
354 | xpos+=pxy*TMath::Cos(fXPhi[iIn]); |
355 | if (xx[iIn]==iClust) xx[iIn]=k; |
356 | } |
357 | (*fY)(0,k)=(*fY)(0,iClust)/(*fPy)(iClust); |
358 | (*fY)(1,k)=(atan2(ypos,xpos)>0) ? atan2(ypos,xpos) : atan2(ypos,xpos)+2*TMath::Pi(); |
359 | (*fY)(3,k)=(*fY)(3,iClust); |
360 | k++; |
361 | } |
362 | } |
363 | nk=k; |
364 | } |
365 | |
366 | //----------------------------------------------------------------------------------- |
367 | void AliDAJetFinder::StoreJets(Int_t nk,Int_t *xx) |
368 | { |
369 | //evaluate significant clusters properties |
370 | const Double_t pi=TMath::Pi(); |
371 | Double_t dMeanDist=TMath::Sqrt(4*fHeader->GetEtaCut()*pi/fNin); |
372 | Bool_t *isJet = new Bool_t[nk]; |
373 | Double_t *etNoBg= new Double_t[nk]; |
374 | Double_t *dDeltaEta=new Double_t[nk]; |
375 | Double_t *dDeltaPhi=new Double_t[nk]; |
376 | Double_t *surf = new Double_t[nk]; |
377 | Double_t *etDens= new Double_t[nk]; |
378 | for (Int_t iClust=0; iClust<nk; iClust++){ //clusters loop |
379 | isJet[iClust]=false; |
380 | Double_t dEtaMin=10.,dEtaMax=-10.,dPhiMin=10.,dPhiMax=0.; |
381 | for (Int_t iIn=0; iIn<fNin; iIn++){ |
382 | if (xx[iIn]!=iClust) continue; |
383 | if (fXEta[iIn] < dEtaMin) dEtaMin=fXEta[iIn]; |
384 | if (fXEta[iIn] > dEtaMax) dEtaMax=fXEta[iIn]; |
385 | Double_t dPhi=fXPhi[iIn]-(*fY)(1,iClust); |
386 | if (dPhi > pi ) dPhi-=2*pi; |
387 | else if (dPhi < (-1)*pi) dPhi+=2*pi; |
388 | if (dPhi < dPhiMin) dPhiMin=dPhi; |
389 | else if (dPhi > dPhiMax) dPhiMax=dPhi; |
390 | } |
391 | dDeltaEta[iClust]=dEtaMax-dEtaMin+dMeanDist; |
392 | dDeltaPhi[iClust]=dPhiMax-dPhiMin+dMeanDist; |
393 | surf[iClust]=0.25*pi*dDeltaEta[iClust]*dDeltaPhi[iClust]; |
394 | etDens[iClust]=(*fY)(3,iClust)/surf[iClust]; |
395 | } |
396 | |
397 | if (fHeader->GetSelJets()){ |
398 | for (Int_t iClust=0; iClust<nk; iClust++){ |
399 | if (!isJet[iClust]){ |
400 | Double_t etDensMed=0.; |
401 | Double_t etDensSqr=0.; |
402 | Int_t norm=0; |
403 | for (Int_t iClust1=0; iClust1<nk; iClust1++){ |
404 | if(iClust1!=iClust){ |
405 | etDensMed+=etDens[iClust1]; |
406 | etDensSqr+=TMath::Power(etDens[iClust1],2); |
407 | norm++; |
408 | } |
409 | } |
410 | etDensMed/=TMath::Max(norm,1); |
411 | etDensSqr/=TMath::Max(norm,1); |
412 | Double_t deltaEtDens=TMath::Sqrt(etDensSqr-TMath::Power(etDensMed,2)); |
413 | if ((*fY)(3,iClust) > (etDensMed+deltaEtDens)*surf[iClust]) isJet[iClust]=kTRUE; |
414 | etNoBg[iClust]=(*fY)(3,iClust)-etDensMed*surf[iClust]; |
415 | } |
416 | } |
417 | for (Int_t iClust=0; iClust<nk; iClust++){ |
418 | if (isJet[iClust]){ |
419 | Double_t etDensMed=0; |
420 | Double_t extSurf=4*fHeader->GetEtaCut()*pi; |
421 | for (Int_t iClust1=0; iClust1<nk; iClust1++){ |
422 | if (!isJet[iClust1]) etDensMed+=(*fY)(3,iClust1); |
423 | else extSurf-=surf[iClust1]; |
424 | } |
425 | etDensMed/=extSurf; |
426 | etNoBg[iClust]=(*fY)(3,iClust)-etDensMed*surf[iClust]; |
427 | } |
428 | } |
429 | } else { |
430 | for (Int_t iClust=0; iClust<nk; iClust++) isJet[iClust]=true; |
431 | } |
432 | delete [] etDens; |
433 | delete [] surf; |
434 | |
435 | //now add selected jets to the list |
436 | Int_t *inJet = new Int_t[fNin]; |
437 | TRefArray *refs = 0; |
438 | Bool_t fromAod = !strcmp(fReader->ClassName(),"AliJetAODReader"); |
439 | if (fromAod) refs = fReader->GetReferences(); |
440 | for (Int_t iClust=0; iClust<nk; iClust++){ //clusters loop |
441 | if (isJet[iClust]){ //choose cluster |
442 | Float_t px,py,pz,en; |
443 | px = (*fY)(3,iClust)*TMath::Cos((*fY)(1,iClust)); |
444 | py = (*fY)(3,iClust)*TMath::Sin((*fY)(1,iClust)); |
445 | pz = (*fY)(3,iClust)/TMath::Tan(2.0 * TMath::ATan(TMath::Exp(-(*fY)(0,iClust)))); |
446 | en = TMath::Sqrt(px * px + py * py + pz * pz); |
447 | AliAODJet jet(px, py, pz, en); |
448 | if (fromAod) |
449 | for (Int_t iIn=0; iIn<fNin; iIn++) if (xx[iIn]==iClust) jet.AddTrack(refs->At(iIn)); |
450 | AddJet(jet); |
451 | printf("jet %d, Eta: %f, Phi: %f, Et: %f\n",iClust,jet.Eta(),jet.Phi(),jet.Pt()); |
452 | } |
453 | } |
454 | delete [] dDeltaEta; delete [] dDeltaPhi; |
455 | delete [] etNoBg; |
456 | delete [] isJet; |
457 | delete [] inJet; |
458 | } |
459 | |
460 | //----------------------------------------------------------------------------------- |
461 | Double_t Dist(TVectorD x,TVectorD y) |
462 | { |
463 | // Squared distance |
464 | const Double_t pi=TMath::Pi(); |
465 | Double_t dphi=TMath::Abs(x(1)-y(1)); |
466 | if (dphi > pi) dphi=2*pi-dphi; |
467 | Double_t dist=pow(x(0)-y(0),2)+pow(dphi,2); |
468 | return dist; |
469 | } |