Meeting 10 22

V0 Daughter PID by TPC dEdx:pp real data By AliTPCIDResponse (aleph parameterization), 4 sigma Daughter PID cuts With this K0s candidates, Momentum resolution can be checked

TPC PID pp MC TPD dedx 3 sigma cut aleph parameterization e PID band sigma parameterized as Signal*a*sqrt(1+b/Npoint)

PID by TPC dEdx: pp MC data LHC10d1 There are significant contamination at high pt when only TPC dEdx is used as PID. DCAxy<3 & DCAz<3 is also applied to eliminate Sec. Particle, which is not used at V0 daughter candidate P >antiP PID contamination Proton from lamda

Cut list: Event cut VertexTracks contributor > 1 If not VertexSPD contributor > 1 Abs(VertexZ) <10 Track Cut for AliESDtrackCuts::TPCOnlytrack Qaulity no kink, NclsTPC>80, chi2TPC/NclsTPC<4 Primary DCA2VertexXY<3, DCA2VertexZ<3 PID cut(use InnerParam Ptot) Other choice for AliESDtrack itself Global track TPCrefit & ITSrefit, SPDhit Qaulity and Primary Additional Qaulity cut  Cov matrix max value, SPD 1 hit Tight Primary cut  DCA2VertexXY < sigma PID cut

2sigma

Global track 3 sigma

PbPb TPC track 3 sigma

PbPb Global track 3 sigma

TOF: pp real data LHC10b pass2 Beta = track length / time Track length is measured by TPC Time = tof measured time (track by tack) - T0 T0 is measured by all tracks tof information If track geometry is known, Time can be predicted with mass hypothesis  Next page

TOF signal

TOF signal 2 Resolution parameterization can be fine, but integrated time reconstruction does not work well.. for electron & muon

Offline V0 Finder Cut optimization for PbPb I do not know about the strategy of V0 Finder at reconstruction, anyway I check Topology cut at PbPb Analyze events 1074 event LHC10e18b centrality 3-6 Daughter candidate selection: Pion from MC info, no kink, kTPCrefit, kITSrefit, TPCncls>80, TPChi2/nclus 0.2, Eta 0.07  8000 tracks in a event  selection without dca cut, 1500 p/n daughter candidates  with dca, candidates pick up K0s among above candidate using MC info N of acceptable K0s in a event is ~ 13

0.06 (0.5) 1 – 6 ( ) 0.07 (0.05) (0.99)

Use AliKFParticle:AliKFParticleBase class I do not understand AliKFParticle Chi2 cut logic, but maybe useful Left : with topology cut and V0 pt >0.2, Rap,0.75 Large BG and small peak Right: findable K0s, no combinatorial BG Topology cut must be optimized. Daughter p selection can be loose(ITSrefit  SPD cluster>1)

0.06 (0.5) 0.07 (0.05) (0.99) 1 – 6 ( )

Daughter dca > 0.5 # 0f findable K0s  %

Invariant mass spectrum for each pt bin

Gaus + pol2 fit can be done with careful choice of parameter& fitting range Pt 0.15GeV – 1.85 GeV, 0.5GeV binning  N of entry within 4 sigma = Ntot  N of BG is calculated by (integral pol2 within 4 sigma)/(Minv binning)

Cut criteria can depend on centrality(multiplicity) & K0s pt, but also daughter pt Because the BG become larger at high pt daughter PID But At first, calculate dNtotal/dpt, S/(S+B) vs pt, dN/dpt with MC PID and daughter pt independent cut Small statistics ?? I do not know how to estimate statistical error..

HIJING 0-3

HIJING 6-9

HIJING 9-12, 12-15

Subtract Method I do not have a guideline for cut optimization.. Verify the method to estimate BG contribution to v2 using dummy POI RP is defined by all tracks or by all tracks – POI daughters or by all tracks – POI daughters – dummy POI

Simultaneous fit Method (invariant mass for resonance and quality cut for pi,k,p) Scan value of invariant mass cut to POI in order to estimate BG contribution to v2 RP is defined by all tracks or by all tracks – POI daughters or by all tracks – daughters of All K0s candidate Number of POI in each event is quite small, so auto correlation effects

q-distribution method: how v2 effect on q vector distribution Qn` = Qn – vn*M*(Cos(n  RP), Sin(n  RP)) This property is also important for LYZ and BT method Distribution is almost Gaussian because resol parameter = v2*sqrt(M) is large To extract v2, NOfRP must be fixed In STAR analysis, NOfRP = 368,568,768 for 0-5% Au-Au W =1

q-distribution method: how v4 effect on q vector distribution

RP with some pt window

Likelihood method to estimate ebe v2 Q vector distribution and Fit response function