Electron Identification with the ALICE TRD

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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 (pgt1GeV)
-0.9lt?lt0.9
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 gt 1 GeV/c
• Fast (6 µs) trigger for high-pt
• Particles (pt gt 3 GeV/c) PID
• Parameters
• 540 modules ? 767 m2 area
• 18 supermodules
• 6 layers, 5 longitudinal stacks
• Length 7 m
• 28 m3 Xe/CO2 (8515)
• 1.2 million read out channels
• 15 TB/s on-detector bandwidth

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Physics with the TRD
TPC dE/dx7 resolution

• 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 cb production x-section
• TRD alone
• L1 trigger on high-Pt particleselectron
  identification Factor 100 Enhancement of
  potentially interesting events (PbPb).
• Upsilon enrichment
• Jets Study jet quenching under LHC conditions

TRD pion efficiency
Test beam data 90 electron efficiency
Goal
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Quarkonia performance
Central Barrel Pt-resolution
B 0.5 T
Dpt/pt lt 2 up to 10 GeV/c lt 9 up to
100 GeV/c
Signal/Background
Significance
Phd. thesis Tariq Mahmoud, Heidelberg
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What is new at LHC
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Read Out Chambers

• Large area chambers (1-1,7 m²)
• -gt need high rigidity
• Low rad. length (15Xo)
• -gt low Z, low mass material
• -gt 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)

• QA
• Standardized chamber building prescription
• Chambers have to pass well defined set of Quality
  control steps

Dubna
2d gain uniformity
Bukarest
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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

PASA
TRAP

• Readout boards last design changes
• Integration of electronics on chambers ongoing

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Electron ID
Typical signal of single particle
LQ Method Likelihood with total charge
LQX Method 2d-Likelihood Total charge
position of maximum cluster
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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, arXivphysics/0506202v1
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PID with Neural Network II
So far analysis done for Testbeam data with 4
small prototype chambers -gtextrapolation 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
Electrons Pions
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Position/Angle Resolution
Angle Resolution lt0.5
Position resolution (y) 200-300 micron
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Pion efficiency
Pion efficiency slightly worse than in previous
test beam
Pions
Points 2002 data Lines 2004 data
2004 Test beam data compared to 2002 Test beam
data Somewhat worse separation
Electrons
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Transition radiation
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