SLIDE 1
Motivation
2
Readout data volume of different detectrors from run 138442 (LHC10h)
TPC performance with HLT clusters Markus Khler Gesellschaft fr - - PowerPoint PPT Presentation
TPC performance with HLT clusters Markus Khler Gesellschaft fr - - PowerPoint PPT Presentation
TPC performance with HLT clusters Markus Khler Gesellschaft fr Schwerionenforschung, Darmstadt Motivation Readout data volume of different detectrors from run 138442 (LHC10h) Bandwidth is a limitating factor for Pb-Pb data taking 2 Data
SLIDE 2
SLIDE 3
3
Data Compression
central Pb-Pb event ~ 80 MB Bandwidth limit ~ 4 GB/s Assume 200 Hz central ~ 16 GB/s
A data compression factor of ~4-6 is needed in the TPC
SLIDE 4
In this talk
4
From the information presented in this talk the usage of HLT clusters for TPC reconstruction is acceptable.
What is the effect on the TPC reconstruction performance when using HLT clusters as input?
SLIDE 5
Compare offline reconstruction with raw and HLT input (clusters)
5
Observables
- Agreement on cluster level
- Agreement on track level
- DCAr vs pT
- DeltaPhi/SigmaPhi (pT resolution)
- dE/dx performance
Input data sets
- Pb-Pb data 2011 (Run 166532)
- Monte Carlo for pp and Pb-Pb
Reconstruction and QA (locally at GSI)
➔AliRoot Release branch 5-01
SLIDE 6
Number of Cluster per Track
6
Pb-Pb 2011 Run 166532
➔To optimize pT resolution
single pad clusters are removed from tracking with HLT clusters
Same track cuts imply (e.g. #clusters) imply different amount of tracks
SLIDE 7
Number of Cluster used for dEdx
7
Pb-Pb 2011 Run 166532
To optimize dE/dx performance single pad clusters are used for dE/dx calculations
SLIDE 8
#cls/#clsFindable
8
Pb-Pb 2011 Run 166532
➔When removing single pad
clusters less clusters per track are found (has been shown in previous TPC meetings)
SLIDE 9
Energy loss in TPC
9
Pb-Pb 2011 Run 166532
HLT
- ffline
SLIDE 10
dE/dx separation with OROC
10
Pb-Pb 2011 Run 166532
momentum Electron-Pion Separation = 6.84 +/- 0.06 Electron-Pion Separation = 6.87 +/- 0.06
Separation are comparable for HLT and offline
SLIDE 11
Comparison of global tracks to constrained TPC tracks
11
Pb-Pb 2011 Run 166532
- ffline
HLT DeltaPhi/SigmaPhi No bias due to HLT cluster
SLIDE 12
DCAz
12
Not enough tracks to calibrate optimal!
(Only about 700 Pb-Pb MB events so far)
SLIDE 13
DCAr vs different quantities
13
Problems of last week solved !
Pb-Pb 2011 Run 166532
SLIDE 14
14
DCAr resolution vs pT
Corresponds to pT resolution in Monte Carlo
Pb-Pb 2011 Run 166532
SLIDE 15
pT resolution from MC
15
HLT
Pb-Pb pp Pb-Pb (Different pT range for pp and Pb-Pb)
High-pT enriched data samples for MC studies in pp and Pb-Pb
SLIDE 16
Summary
16
- The TPC performance for HLT and offline cluster finders was
presented
➔Not shown here : LHC10e, LHC10h, LHC11c (data) ➔Not shown here : pp MB, Pb-Pb (central)
- pT resolution
➔For 2011 Pb-Pb data (Run 166532)
- Cluster variables
- Track variables
- DCAr vs pT (corresponds to pT resolution)
- dE/dx separation (for OROC only)
- Many more observables were shown
Many more parameters are analyzed
➔For high-pT samples Monte Carlo in pp and Pb-Pb
SLIDE 17
Conclusion
17
The usage of HLT clusters is acceptable for TPC reconstruction
The High Level Trigger cluster finder can significantly increase the collectible statistics
- f ALICE in Pb-Pb data taking 2011
SLIDE 18
Involved people
18
Artur Szostak, Jacek Otwinowski, Jochen Thaeder, Marian Ivanov, Markus Koehler, Michael Knichel, Matthias Richter, Sergey~Grobunov, Timo Breitner, Thorsten Kolleger, Torsten Alt, Weilin~Yu, Alberica Toia
SLIDE 19
Backup (DCAr vs pT)
19
- ffline