CERN Sept 2010 beam test: Sensor response study

1. CERN Sept 2010 beam test: Sensor response study Chris Walmsley and Sam Leveridge (presented by Paul Dauncey) Paul Dauncey

2. CERN 2010 beam test data • Taken with EUDET telescope • Two arms, each of three layers of MIMOSA-26 silicon pixel sensors • Ran with and without tungsten sheets mounted between the arms • MIMOSA-26 sensors • Digital CMOS pixel sensors, fabricated with 0.35mm AMS • 700mm thick (TBC) with 15mm epitaxial layer. • 1152×576 regular (i.e. no gaps) pixel array • Pixel pitch 18.4mm, total active area 21.2×10.6mm2 • Rolling shutter readout; each step is a group of 72 pixels in single row so 16 steps per row and 9216 steps for whole sensor • Rolling shutter time ~110ms total, so ~100ns per step • Data from two full rolling shutters around trigger are kept Paul Dauncey

3. Sensor study motivation • Overall aim of beam test is to measure EM shower density • Form tracks in front three sensor layers • Project through tungsten to back three sensors layers • Measure particle density w.r.t. track projection position • Clusters of hits are used for two purposes • In front three layers to form track hits • In back three layers to count particles and know their position • For both need to know how a single particle forms a cluster • Determine by studying rates of various patterns of hits in clusters • Want average particle position within cluster for a given pattern • Want resolution of particle position for a given pattern Paul Dauncey

4. Cluster patterns • Cluster pattern depends on where the particle passes through • If near centre, one pixel in cluster • If near centre of edge, two pixels in cluster • If near corner, four pixels in cluster • Classify by number of pixels and pattern • Some specific patterns are “physical”; all can be rotated/reflected • Examples of other patterns which are not physical Paul Dauncey

5. Cluster patterns in data • Form track in the five “other” layers • Project into layer being studied and look for nearby clusters • Look at rates of clusters with various patterns • See non-negligible rate of a pixel firing twice in one event • Presumably this is due to real particle hit • Integrate over both time frames • Only count number of different pixels, not number of times fired, when characterising the clusters • Look at runs 20021-26 • All are 120GeV hadrons with no tungsten • Different threshold per run; 6.0-10.5 (in some arbitrary threshold units) Paul Dauncey

6. Distance of cluster from track projection • Clean; almost no background even at threshold=6 • 5-hit tracks difficult to fake • Alignment looks OK; all centred on zero Paul Dauncey

7. Cluster simulation • 2D simulation of charge spread and threshold • Standalone model of 5×5 pixel array • Put “charge” with Landau distribution at point; units MPV=1 • Initial position varied uniformly over 100×100 array in central pixel • Parameterise diffusion by spreading with Gaussian • Charge absorbed per pixel is integral of Gaussian over pixel area • Add noise per pixel and apply threshold • Tune parameters • Diffusion spread Gaussian width • Threshold units-to-MPV conversion factor • Pixel noise • Minimise chi-squared comparison with data cluster pattern distribution Paul Dauncey

8. Tuning example Layer 0, threshold = 10.0 units • Chi-sq versus two parameters • Noise fixed to 2.4 threshold units • X axis is spread Gaussian width in % of pixel pitch • Y axis is conversion factor Sim Data 8% inefficiency Paul Dauncey

9. Layer 0, threshold=6 Paul Dauncey

10. Layer 0, threshold=8 Paul Dauncey

11. Layer 0, threshold=9 Paul Dauncey

12. Layer 0, threshold=10.5 Paul Dauncey

13. Each layer tuned to all runs Paul Dauncey

14. Tuning results • Parameters reasonable consistent for all thresholds and for five of the six layers • Spread Gaussian width ~ 25-30% ~ 5mm • Threshold unit-to-MPV conversion ~ 0.03-0.04 • E.g. threshold = 10 units ~ 0.3-0.4MPV; when charge split between four pixels, efficiency will be low • Noise ~ 2.4 threshold units ~ 0.08MPV • Layer 3 significantly different in conversion parameter • Spread width and noise similar • Threshold unit-to-MPV conversion ~ 0.09 • E.g. threshold = 9 units ~ 0.8MPV; generally will have low efficiency) • Annoying as this is closest layer to tungsten in shower runs Paul Dauncey

15. Layer 3, threshold=9 42% inefficiency! Paul Dauncey

16. Simulation positions for patterns Not layer 3 Paul Dauncey

17. Cluster-track positions for N=0 • Left: Standalone simulation (no track, perfect position) • Middle: Data (including track resolution) ~ 100k events • Right: First go at full simulation (including tracking resolution) ~ 100k events Paul Dauncey

18. Cluster-track positions for N=1, 2 line Paul Dauncey

19. Cluster-track positions for N=3 L, 4 square Paul Dauncey

20. Cluster resolution • Cross-check simulation using resolution • Each cluster pattern results from particle hitting particular position within pixel • Look at track projection positions for each cluster pattern • Look at resolution from simulation and data (subtracting track resolution) for each pattern • Resolve resolution along symmetry axes a a a b b b Paul Dauncey

21. Layer 1 cluster-track residuals • Layer 1 has smallest track resolution • Histograms show a (solid) and b (dashed) residuals in each category Paul Dauncey

22. Layer 1 resolutions • Left: Data, right: full simulation; a and b resolutions in each category • Red is raw RMS, blue is with track resolution subtracted • Qualitative agreement, except category 4 (N=3L) is quite different • Not understood.... • Quantitative agreement to ~20% level for others Paul Dauncey

23. One other oddity not understood... • Rolling shutter readout with two frames per event • Cluster hits should appear in frame 0 if in high rows and frame 1 if in low rows; depends on trigger (“pivot”) time Full simulation Data, p high rate Data, e low rate Paul Dauncey

24. Conclusions and future work • Sensor simulation seems to give good agreement • Gives right cluster shapes and reasonable resolutions • Chris and Sam will finish at the end of March • Some tweaking of simulation parameter tuning • Obtain standalone simulation resolutions • Next steps (by me...) • Interface fully to GEANT4 simulation • Model positron showers as taken in real data • Measure apparent density of shower hits in layers 3,4,5 • Correct using simulation to true density at back of tungsten block Paul Dauncey