Electron Identification with the ALICE TRD Clemens Adler Physikalisches Institut Heidelberg For the TRD collaboration HCP2005, Les Diablerets, July,

ALICE TRD: Identification of electrons (p>1GeV) -0.9<η<0.9 ITS TPC TRD

ALICE TRD principle

TRD in numbers Purpose:  Electron ID in the central barrel at p > 1 GeV/c  Fast (6 μs) trigger for high-p t Particles (p t > 3 GeV/c) +PID Parameters:  540 modules → 767 m 2 area  18 “supermodules”  6 layers, 5 longitudinal stacks  Length: 7 m  28 m 3 Xe/CO 2 (85:15)  1.2 million read out channels  15 TB/s on-detector bandwidth

Physics with the TRD Together with TPC and ITS (dE/dx, good momentum resolution), the TRD provides electron identification sufficient to study:  Di-electron channel: production of J/Psi, Upsilon and continuum (complementary to muon arm measurement). + Displaced vertex from ITS:  E.g. Identify J/Psi from B decays  Single electron channel: semi-leptonic decays of open charm and beauty:  Handle on c+b production x-section TRD alone:  L1 trigger on high-Pt particles+electron identification: Factor 100 Enhancement of potentially interesting events (PbPb).  Upsilon enrichment  Jets: Study “jet quenching” under LHC conditions TPC dE/dx:~7% resolution TRD pion efficiency Test beam data: 90% electron efficiency Goal

Quarkonia performance Phd. thesis Tariq Mahmoud, Heidelberg  p t /p t < 2% up to 10 GeV/c < 9% up to 100 GeV/c B = 0.5 T Central Barrel Pt-resolution Signal/BackgroundSignificance

What is new at LHC Plenty of c+b to start with central AA Upsilon suppression should be observable at LHC RHIC LHC hard gluon induced quarkonium breakup hep- ph/ Complete primary J/Psi suppression expected Strong (centrality dependant) secondary J/Psi production (statistical hadronization) ->strong QGP Signal

Read Out Chambers  Large area chambers (1-1,7 m²)  -> need high rigidity  Low rad. length (15%Xo)  -> low Z, low mass material -> Carbon reinforced sandwich construction

Read out chambers II 5 chamber production sites: –Bucharest (NIPNE) –Dubna (JINR) –GSI (Darmstadt) –Heidelberg (University) –Frankfurt (University) Dubna Bukarest QA: –Standardized chamber building prescription –Chambers have to pass well defined set of Quality control steps 2d gain uniformity

Electronics 1.2 million channels 18 channels in 1 MCM 16(+1) MCMs per readout board (4104 pc.) CPUs working in parallel during readout

Electronics Status PASA and TRAP chips ready PASA: have full quantity TRAP: several Wafers Readout boards: last design changes Integration of electronics on chambers ongoing PASATRAP

Electron ID LQ Method: Likelihood with total charge Typical signal of single particle LQX Method: 2d-Likelihood: Total charge + position of maximum cluster Likelihood distribution Extract probabilities Integrated Charge Total charge spectra Depos. Energy (keV) Counts Max. cluster position Distribution of maximum cluster position

PID with Neural Network I Each neuron of one Layer is connected to every neuron of the following Layer. Input Layer: Charge per timebin One hidden Layer: 22 neurons Output layer per chamber: Probability to be Electron/Pion Connect 6 Chambers by NN, or multiplication of Probabilities. Submitted to NIM A, arXiv:physics/ v1

PID with Neural Network II So far analysis done for Testbeam data with 4 small prototype chambers ->extrapolation to 6 Chambers Momentum dependence of Pion efficiency To do: Test with higher statistics and on generalized dataset (new Testbeam data) Try to understand this significant improvement analytically

Testbeam Oct small size prototype chambers (Transition radiation spectra measurement). 6 real size production chambers (2 different size types) (Almost) final electronics

Signal in production chambers Online Event display Electrons Pions

Position/Angle Resolution Large chambers Prototype Position resolution (y): micron Angle Resolution: <0.5°

Pion efficiency 2004 Test beam data compared to 2002 Test beam data: Somewhat worse separation Pions Electrons Points: 2002 data Lines: 2004 data Pion efficiency slightly worse than in previous test beam

Transition radiation Energy spectrum data simulation Number of produced TR photons with different Radiators Regular: foil stacks Sandwich: ALICE TRD radiator

Online Tracking Comparison: Online tracking ↔ Offline tracking Very Good Agreement! Outliers on per mille level due to Calculation precision Offlilne Online

Summary TRD enhances ALICE Heavy flavour physics capabilities Detector mass production under way. Electronics finalized Electronics Integration in final iteration First Supermodule to be assembled end of the year Testbeam: –Detector performance is well understood and satisfies design considerations Neural network approach: –New test beam data (6 real size chambers, different angles, higher statistics) –Can information used by NN be extracted analytically?

TRD Collaboration Main Contributions: Germany: Frankfurt University (IKF) Gesellschaft für Schwerionenforschung (GSI) Darmstadt Heidelberg University (Physikalisches Institut, Kirchhoff Institut) Münster University (IKP) Russia: JINR Dubna Romania: NIPNE Bukarest Additional Subsystems: Japan: Tokyo University, Nagasaki University Greece: Athens University Germany: FH Köln, University Kaiserslautern, FH Worms, TU Darmstadt