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

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.

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

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.

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.

Simulation VS real data (1)‏ Q: How to check the quality of the digitisation model? A: Compare real to simulated data! In reality : Beam test @ 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.

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

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.

Quantitative comparison of data % of pixels above threshold 0 1 2 3 4 5 6 0 1 2 3 4 5 6 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

Simulation Beam Data Beam/Sim 45 60

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

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

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

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

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

Some results (Preliminary) reconstruction efficiency 1 AuAu central collision @ 25 AGeV MVD station @ 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]

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

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

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

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.

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)‏

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.