Status of calorimeter simulations

1. Status of calorimeter simulations Mikhail Prokudin, ITEP

2. Outline • π˚ reconstruction quality • Cluster finding • requirements • 2 approaches • Shower library • Simplified MC supporting design studies • by A. Chernogorov • Next steps

3. π˚ reconstruction quality • Standard 25GeV AuAu UrQMD event • γfrom same π˚ • Smooth with • 7%/sqrt(E) energy resolution • 3cm position resolution • both RMS 6.669e-3 RMS 2.627e-3 RMS 6.692e-3 3cm position resolution doesn’t affect the reconstruction quality!

4. Fitting routine first approximation energy calibration position S-curves number of photons number of local maximums in cluster iterative procedure shower shape shower library shower width Cluster finding maximize cluster size use all possible information for fitting minimum 3 cells for cluster with 1 maximum 3 degrees of freedom per photon occupancy hadron contamination minimize number of maximums less parameters in fit stability Requirements for cluster finding procedure

5. Cluster unfolding. Naive approach • Color: energy deposition • Edging: • Green: clusters • Red: excluded by charged tracks • Cluster definition: • connected area of cells with energy deposition above the threshold • Threshold ~150MeV BTW, Real tracking!

6. Cluster finding. Naive approach • Cluster definition: • connected area of cells with energy deposition above the threshold • threshold can be found from MC • Occupancy differs ~102 times • partially compensated by segmentation • lost clusters in periphery of calorimeter • create too large clusters in central region • No chance to fit cluster with >7 peaks • central region is lost! • Clusters with size <3 cells require additional information 25 UrQMD events Peaks per cluster ALICE approach

7. Cluster finding • Find peak above threshold • peaks associated with charged track are removed from the consideration • Construct an area with certain number of cells around the peak • it should contain 2x2 submatrix with maximum energy of 3x3 matrix around peak • size determined by • reconstruction quality of single photon • at least 3 cells for reconstruction • at least 4 cells for unfolding • occupancy • Find areas intersecting with given area • Construct a cluster PHENIX-like approach

8. Cluster unfolding • Color: energy deposition • Edging: • Green: clusters • Red: excluded by charged tracks • Less clusters in central region with new algorithm • Cluster size under control

9. Comparison 25 UrQMD events New Naive Peaks per cluster Number of peaks per cluster lower using new approach

10. Conclusions for clustering algorithm • Naive approach does not work • occupancy variations • New (PHENIX-like) approach • number of peaks in cluster under control • less sensitive to occupancy • Real tracking

11. Shower shape • Analytical formula for shower shape approximation • ALICE and PHENIX experience • No memory consumption • Best for multicore CPU • Poor quality for large incident angles • Shower library • Fits exactly to the data • Requires a lot of memory • (and CPU!) • Any incident angle • checking analytical approximation quality • Shower width • Also needed for fitting procedure • Approximation or storing in library?

12. Shower library • ECAL with very high segmentation (1x1mm2 volumes) • use one shower multiple times • volumes merging procedure • Transport photons for every: • Energy • Theta • Phi • Eightfold decrease due to symmetry • For low energies we have to generate more showers • Larger fluctuations for low energy showers • 10k for 0.49 GeV • 2k for 16GeV

13. During shower library creation Faster during reconstruction Also can store RMS (see next slide) Bigger library size Cell size fixed Different data set for each cell size During reconstruction Homogenous dataset Cell size not fixed Information about RMS is lost Need analytical approximation for errors More CPU required during reconstruction Volumes merging

14. Shower width h4 • Energy deposition in cluster cells are not independent • RMS value storing useless • Need an analytical formula • correlations should be in! • … h5 h5 h4

15. Shower library • 170 Mb disk space • 300 Mb in memory • Energy: 0.49, 1, 2, 4, 6, 9, 12, 16 GeV • Theta: 2˚, 3.5˚, 5˚, 6.5˚, 8˚, 9.5˚, 11˚, 13˚, 16˚, 20˚, 24˚, 28˚, 32˚ • Phi: 0˚, 10˚ , 20˚, 30˚, 40˚ 6x6cm2 cells See CbmShowerLib class

16. Shower library 12x12cm2 cells • Shower rotating on fly? • classical trade CPU vs. memory

17. Simplified MC supporting design studies Steel band effects • Constructive details • steel band • dead material in front • fibers insertion • Necessarily for design works • can be used in CBMROOT simulation Ex. 1. SS bands between modules on the ECAL response. Eγ=16 GeV, θ=3° Pb=Sc=1mm. 1- ▲, 2-*, 3-● by A. Chernogorov

18. ECAL γ 5m Dead material in front of the calorimeter Example: Fe, 5m • Calorimeter • 200 layers • 1 mm Pb + 1 mm Sc • Absorber • C, Fe, and Cu studied • 0.0 X0, 0.2 X0, 0.4 X0, 0.8 X0 • distance to ECAL 2-10m by A. Chernogorov

19. Conclusions and next steps • Real tracking in calorimeter • Studied different approaches to cluster finding • naive one does not work • Shower library is complete • realization in SVN and AUG07 • Unfolding procedure under (extensive) development • analytical formula for shower width • with correlations • can be submitted to SVN any moment • Design supporting MC studies under way • by A.Chernogorov

20. First approximation energy calibration position S-curves Cluster unfolding cluster finding procedure fitting routine shower shape shower library LHCb like methods Pure γ, no background Simple and easy to check Test site for shower library routines Can be done in few month Procedure of γ reconstruction Done • ALICE PHENIX-like methods • Require much more effort • CALO parameters should be fixed? From September 2006 CBM collaboration meeting From September 2006 CBM collaboration meeting