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PHOS off-line status

PHOS off-line status. ALICE off-line meeting 9-13 September 2002. Yuri Kharlov Subatech/IN2P3 & IHEP/Protvino (for the PHOS off-line team). PHOS geometry. Acceptance: 0.12, 100 Construction: 5 modules EMC+CPV EMC: 64  56 cells each module EMC cell: 2.2  2.2  18 cm 3 PbWO 4

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PHOS off-line status

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  1. PHOS off-line status ALICE off-line meeting 9-13 September 2002 Yuri Kharlov Subatech/IN2P3 & IHEP/Protvino (for the PHOS off-line team)

  2. PHOS geometry • Acceptance:0.12, 100 • Construction: 5 modules EMC+CPV • EMC: 6456 cells each module • EMC cell: 2.22.218 cm3 PbWO4 • CPV: 12856 cathode pads, anode wires with 5.56 mm pitch

  3. PHOS geometry in ALIROOT

  4. PHOS within ALICE

  5. Code development (as of September 2002) Simulation • We work at Linux RH 7.2, RH 7.3 • We use gcc 3.2 • We use root 3.03-08 • We use always CVS HEAD of AliRoot • AliPHOSv0: real geometry, passive material (no hits) • AliPHOSv1: as AliPHOSv0 + hits hit: x,y,z,Eloss,Id,primary (one hit per primary per cell) • AliPHOSvImpact: as AliPHOSv1 + impacts impact: x,y,z,p at the detector ’s upper surface • AliPHOSvFast: fast simulation (not really used)

  6. Reconstruction • Whole reconstruction chain from Hits to RecParticles works in split and non-split mode • Wrapper class for the reconstruction chain: AliPHOSReconstructioner • All tasks are created analogously: AliPHOS<task>(simulated file, branch name, split/no-split)

  7. Reconstruction user case: non-split mode After simulation: galice.rootwith gAlice, geometry, TreeE, TreeH • s=new AliPHOSSDigitizer(“galice.root”) • s->Exec(“deb all tim”) • d=new AliPHOSDigitizer(“galice.root”) • d->Exec(“deb all tim”) • c=new ALiPHOSClusterizerv1(“galice.root”) • c->Exec(“deb all tim”) • t=new AliPHOSTrackSegmentMakerv1(“galice.root”) • t->Exec(“deb all tim”) • p=new AliPHOSPIDv1(“galice.root”) • p->Exec(“deb all tim”) After reconstruction: the same galice.rootwith TreeS, TreeD, TreeR filled

  8. Reconstruction user case: split mode After simulation: galice.rootwith gAlice, geometry, TreeE, TreeH • s=new AliPHOSSDigitizer(“galice.root”,”1”,kTRUE) • s->Exec(deb all tim”) TreeS is written to PHOS.SDigits.1.root • d=new AliPHOSDigitizer(“galice.root”,”1”,kTRUE) • d->Exec(deb all tim”) TreeD is written to PHOS.Digits.1.root • c=new AliPHOSClusterizer(“galice.root”,”1”,kTRUE) • c->Exec(deb all tim”) TreeR with Rec.Points is written to PHOS.RecData.1.root • t=new AliPHOSTrackSegmentMakerv1(“galice.root”,”1”,kTRUE) • t->Exec(deb all tim”) TreeR with TrackSegments is written to PHOS.RedData.1.root • p=new AliPHOSPIDv1(“galice.root”,”1”,kTRUE) • p->Exec(deb all tim”) TreeR with Rec.Particles is written to PHOS.RedData.1.root No gAlice, TreeE, TreeK, TreeH, geometry is written to the split files

  9. Reconstruction (continued) • Reconstruction can be performed either by a script calling tasks one-by-one, or • Reconstruction wrapper: all-in-one • r=new AliPHOSReconstructioner(file,branch,split) • r->ExecuteTask(“deb”) • PHOS reconstruction is not compatible with AliRunDigitizer in a split mode because gAlice is missing in split files • Mixing events at SDigits level: in AliPHOSDigitizer::MixWith(another file)

  10. EMC performance (1) Energy resolution Position resolution Simulation reproduces measurements

  11. EMC performance (2) Rec.point shift due to inidence angle Effective shower maximum depth vs E

  12. EMC performance (3)

  13. CPV performance (1) x-resolution z-resolution EMC-CPV distance Simulation reproduces measurements

  14. CPV performace (2) Matching probability of charged particles with EMC rec.point 10% of  are lost having the minimal material (with holes in TRD and TOF) 90% of e- give matching EMC-CPV rec.points

  15. Particle identification in PHOS (1) PHOS identifies particles by: • time-of-flight • distance between CPV and EMC rec.points • shower shape in EMC (2 approaches): • - at very high energies neural networks can be used • - at any energies principal components analysis can be applied

  16. Particle identification in PHOS (2) Photon/0 identification with a Neural Network • Reconstructed particles are defined by E, 1, 2,M10, M30, M40, M04,  • NN response S(0,1) classifies clusters. • (,) varies from 90% to 20% at E=20-120 GeV • (,0) is 1-3% in this range

  17. Particle identification in PHOS (3) Photon identification with the Principal Components Analysis Rec.point is characterized by the following parameters: • Lateral dispersion • Shower ellipse axes • Sphericity • Core energy • Largest energy fraction in one crystal TPrincipal reduces all parameters to 2 significant ones

  18. Our plans and needs • Global tracking: • reconstructed tracks in ITS+TPC can be propagated to PHOS to improve CPV-EMC matching and track segment making • finish PID • Adopt aliroot for bea-test data analysis • Implement data bases for dead modules and calibration coefficients

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