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Electron Identification with the ALICE TRD Clemens Adler Physikalisches Institut Heidelberg For the TRD collaboration HCP2005, Les Diablerets, July, 6 2005
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ALICE TRD: Identification of electrons (p>1GeV) -0.9<η<0.9 ITS TPC TRD
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ALICE TRD principle
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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
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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
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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
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What is new at LHC Plenty of c+b to start with
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What is new at LHC Plenty of c+b to start with RHIC LHC hard gluon induced quarkonium breakup hep- ph/0311048 Complete primary J/Psi suppression expected
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What is new at LHC Plenty of c+b to start with RHIC LHC hard gluon induced quarkonium breakup hep- ph/0311048 Complete primary J/Psi suppression expected Strong (centrality dependant) secondary J/Psi production (statistical hadronization) ->strong QGP Signal
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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/0311048 Complete primary J/Psi suppression expected Strong (centrality dependant) secondary J/Psi production (statistical hadronization) ->strong QGP Signal
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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
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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
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Electronics 1.2 million channels 18 channels in 1 MCM 16(+1) MCMs per readout board (4104 pc.) 260 000 CPUs working in parallel during readout
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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
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Electron ID Typical signal of single particle
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Electron ID Typical signal of single particle Integrated Charge Total charge spectra Depos. Energy (keV) Counts
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Electron ID LQ Method: Likelihood with total charge Typical signal of single particle Likelihood distribution Extract probabilities Integrated Charge Total charge spectra Depos. Energy (keV) Counts
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Electron ID LQ Method: Likelihood with total charge Typical signal of single particle Likelihood distribution Extract probabilities Integrated Charge Total charge spectra Depos. Energy (keV) Counts Max. cluster position Distribution of maximum cluster position
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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
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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/0506202v1
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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
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Testbeam Oct. 2004 4 small size prototype chambers (Transition radiation spectra measurement). 6 real size production chambers (2 different size types) (Almost) final electronics
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Signal in production chambers Online Event display
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Signal in production chambers Electrons Pions
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Position/Angle Resolution Large chambers Prototype Position resolution (y): 200-300 micron Angle Resolution: <0.5°
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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
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Transition radiation Energy spectrum data simulation Number of produced TR photons with different Radiators Regular: foil stacks Sandwich: ALICE TRD radiator
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Online Tracking Comparison: Online tracking ↔ Offline tracking Very Good Agreement! Outliers on per mille level due to Calculation precision Offlilne Online
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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?
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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
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