1. IPHC, Strasbourg / GSI, Darmstadt
MVD digitiser
Christina Anna Dritsa
IPHC, Strasbourg / GSI, Darmstadt
Outline:
Motivation
Model
Clustering
Comparison with data
Preliminary results
CBM collaboration meeting 15/10/08

2. Why a digitiser? Define the MicroVertex Detector properties:
What is the optimal pixel size for the CBM-MVD?
What is the occupancy for a given collision energy?
What is the collision pile-up that the CBM-MVD can handle?
What is the maximum beam intensity for CBM-MVD?
Optimize the detector by means of realistic simulation.

3. The operation principle of MAPS
Particle trajectory
P-Well
Epi-layer
Substrate
Preamplifier (one per pixel)‏
~20µm
Diffusing free electrons

4. Digitisation model*: simple description
*Derived from the CMS and ILD simulation software.
No Electric Field:
Electrons are diffusing θ
Digitisation model for non-depeleted detector:
Particle trajectory divided in segments
Energy deposited in each segment is translated into charge
Charge is spread at the surface according to a gauss distribution σ
sensitive volume
Need to adapt the model’s parameters (σ) in order to reproduce experimental data.

5. How a digitiser changes simulations
Before:
The result of a particle passing through detector was a point like hit.
No pixels simulated (no charge on pixels)‏
No threshold on charge of pixel for reading out a pixel
No cluster of pixels
No pile-up of clusters
Now/Soon:
The pixel structure of the MVD is represented.
There is charge distributed on pixels.
Possibility to apply thresholds for cluster reconstruction.
Possibility to simulate an ADC with up to 12 bits for pixel read-out.

6. Simulation VS real data (1)‏
Q: How to check the quality of the digitisation model?
A: Compare real to simulated data!
In reality :
Beam CERN-SPS; pions 120 GeV; Mimosa17 (30μm pitch, 14μm epi); measured angles 0-80 degrees wrt the detector plane; no magnetic field.
In simulation:
BoxGenerator-> shoot pions of 120 GeV, 0-80 degrees, no magnetic field.

7. Simulation VS real data (2) Real data Simulated
Arbitrary color scales 0°
σ ~ 1 μm
σ ~ 50 μm
80°

8. Comments on each parametrisation
Parametrisation A: The present model can reproduce the average shape of the cluster
BUT for a limited number of neighbors
Parametrisation B: The model can reproduce the charge spread
BUT not the cluster shape at large angles (asymmetry in XY- axis)‏
Not correct approach if we want to guess the track
inclination.
For the studies shown next, parametrisation B is used. Reason: More conservative estimation of the occupancy, no need for studying track inclination yet.

9. Quantitative comparison of data
% of pixels above threshold
Pixel index in the central line
In the following example the threshold is 45 electrons: ~3*noise
Histo of Simulation
~= 1
Histo of Beam Data

10. Simulation
Beam Data
Beam/Sim 45 60

11. Cluster Finding Algorithm Acknowledgments to M
Cluster Finding Algorithm Acknowledgments to M.Deveaux for his contribution
Q: How to reconstruct the track position from the cluster?
1) Define charge threshold above which a pixel is a “seed”
2) Identify “seeds” on the detector plane
3) Define charge threshold above which a pixel is a “neighbor”
4) Seek for “neighbors” around the pixel
5) Flag pixels already used
6) When no neighbors are found anymore then save the cluster in a 7x7 array.
7) Hit position is the center of gravity of the charge

12. Cluster Finding Algorithm Acknowledgments to M
Cluster Finding Algorithm Acknowledgments to M.Deveaux for his contribution
Example:
Seed Pixel
Neighbor Pixel

13. Cluster Finding Algorithm Acknowledgments to M
Cluster Finding Algorithm Acknowledgments to M.Deveaux for his contribution
Example:
Seed Pixel
Neighbor Pixel

14. Cluster Finding Algorithm Acknowledgments to M
Cluster Finding Algorithm Acknowledgments to M.Deveaux for his contribution
Example:
Seed Pixel
Neighbor Pixel

15. Cluster Finding Algorithm Acknowledgments to M
Cluster Finding Algorithm Acknowledgments to M.Deveaux for his contribution
Example:
Seed Pixel
Neighbor Pixel

16. Some results (Preliminary) reconstruction efficiency
1 AuAu central collision
@ 25 AGeV
MVD 5cm
Pixel selection threshold ~3*noise
Pixel pitch 30μm
284 reconstructed over
296 true hits
~96% reconstruction efficiency
Efficiency loss because of selection threshold and cluster merging
y [cm]
x [cm]

17. Some results (Preliminary)‏
25AGeV: MVD 5cm
1 AuAu central collision
with delta electrons from 100 Au Ions
30μm pixel pitch
~10% of reconstructed clusters are merged
25AGeV: MVD 5cm
1 AuAu central collision
2 AuAu mbias
with delta electrons from 300 Au Ions
~30% of reconstructed clusters are merged

18. 25AGeV: MVD 5cm
1 AuAu central collision
2 AuAu mbias
with delta electrons from 300 Au Ions
3252 reconstructed hits
~30% of reconstructed clusters are merged

19. Some results (Preliminary)‏
30μm pixels
(Pile-Up)‏

20. Digitiser model summary
A digitisation model for MAPS was implemented
Qualitative comparison between reality and simulation shows the limitation of the existing model in generating both the cluster shape and charge spread.
For a given parametrisation: Quantitative comparison shows good match with reality; the percentage of pixels of the central line (and column) above threshold is similar to the one for real data.
Next steps:
Try different algorithm for the digitiser so that both the cluster shape and charge spread are reconstructed.

21. Performance and limits for cluster finding
All possible cluster shapes can be identified.
Not easy to do hit-matching (more than one track contributing per cluster).
Are all the reconstructed hits real hits?
Almost 95-99% of hits are reconstructed, depending on the thresholds.
Not easy to calculate uncertainty on hit position: set uncertainty to 5μm for all hits
Code is not speed optimised (20s/evt)‏

22. Performance and limits for cluster finding
From preliminary low statistics simulations it seems that cluster merging is not negligible
Next steps:
Study cluster merging with smaller pixels (10μm)‏
Improve the cluster finding algorithm: implement pattern recognition
Further simulations with different MVD geometries and pile-up.