Simulations Working Group Meeting 07.10.08

1. Simulations Working Group Meeting 07.10.08 Trigger Studies Using Stacked Pixel LayersMark Pesaresihttps://twiki.cern.ch/twiki/bin/view/Main/MarkPesaresi

2. Overview • Construction of Strawman B • Creation of a single stacked pixel layer • Construction of multiple layers and configurability of each • Set up configurable pixel sizes • Studies using Strawman B • Simulations within the geometry • Trigger studies using digis Mark Pesaresi

3. Overview • Strawman B Status • Ported successfully to CMSSW_1_8_4 and available in CVS • Latest version includes patches to missing hit problems (see previous talks) • Simulation studies ongoing – focus here is on trigger studies Mark Pesaresi

4. Occupancy Review • Attempted to determine the differences in occupancy reported by the Fast and Full simulations • Discovered that for the pixel layers, the differences are small (~3) and are understood • Fast sim does not take into simulate out-of-time pileup • Fast sim places cuts on minimum track pt and loopers by default • Fast sim does not simulate delta rays • Would be beneficial to extend investigation for any layer, both strip and pixel so that any differences can be parameterised. Occupancy [%] for pixel layers in MinBias events at LHC pileup (~21 interactions/BX) for different Fast and Full simulation scenarios Mark Pesaresi

5. Geometry • Considering a single stacked pixel layer at r=25cm, length=221cm • Current pixel system included in geometry • Outer geometry unnecessary at this point • Using latest version of Strawman B in CMSSW_1_8_4 0.9 Stacked Pixel Layer @ 25cm 2.14 2.5 Current Pixel System Mark Pesaresi

6. Sensor Geometry • Strawman B parameters modified in pixbar.xml and trackerStructureTopology.xml • Sensor choice: tilted at 23° – to reduce cluster width • by minimizing Lorentz drift • 100μm thickness • 28mm x 72.8cm sensor dimensions • z overlap – to fill gaps in z • 100 μm x 2.37mm pixel pitch • 256 x 30 pixels per module • Sensor separation varied between 1-4mm • Modification made to geometry to aid trigger studies – not yet part of StrawmanB • z offset – to match columns • in top and bottom sensors • with increasing eta 23° z overlap z offset Mark Pesaresi

7. Tracking Trigger • Aim is to assess the performance and viability of a stacked pixel layer as part of a L1 tracking trigger by the determination of track pt • Study attempts to simulate the implementation of such a trigger • Generation of trigger primitives using digi information • Performance of the algorithm in finding high pt tracks • Investigate methods of sensor readout and hit correlation for the on-detector implementation • Complements previous study reported on in December using a stacked strip layer in the outer tracker Mark Pesaresi

8. Simulation Overview Stacked Layer Digis [detId, row, column, adc] adcdigi > 30 sorted by detId into modules with upper and lower sensors adc cut & sorting Sorted Digis [detId, row, column, adc] hits between upper and lower sensors are correlated to check for high pt tracks modifiable search window cuts can be applied correlation algorithm Stubs [detIdhigh, rowhigh, columnhigh, adctot, row difference, column difference, simTrackIdhigh, simTrackIdlow] Mark Pesaresi

9. Correlation Algorithm 256 Row difference calculation Since the sensors are tilted, there is a difference between the position of the higher and lower sensor hits for a high pt track which is also dependent on the position of the incident track on the sensor The fixed offset as a function of the row number can be applied to calculate the true row difference Equivalent to an on detector map between the hit position on the higher sensor to a set of positions on the lower sensor pixel row 125 pixel row 114 0 Column difference calculation Column difference is not symmetrical – dependence on whether hit is in detector +/-z. Can be exploited to maximise rate reduction. Mark Pesaresi

10. Correlation Algorithm Stub generation A stub is created when both the row and column difference lie within a given range. Upper e.g. row offset = 3 0 ≤ row window ≤ +1 0 ≤ column window ≤ +1 Lower 100μm 100μm Pass Fail Mark Pesaresi

11. Algorithm Performance Performance of a detector stack at r=25cm for sensors with pitch 100μmx2.37mm. Correlation cuts optimised for high efficiency Max Efficiency: Average maximum efficiency for a high pt track to form a stub. Inefficiencies due to sensor doublet acceptances and algorithmic efficiency (window cuts) Fake: Average fraction of stubs per event generated by correlating hits from different tracks Reduction Factor: Average data rate reduction factor per event (NStubs / NDigis) where NDigis is number of hits with charge >adcdigi for the whole stacked layer Mark Pesaresi

12. Algorithm Performance Performance of a detector stack at r=25cm for sensors with pitch 100 μmx2.37mm. Correlation cuts optimised for high efficiency Max Efficiency calculated using 20,000 single 50GeV Muon/Antimuon events with smearing Fake/Reduction Factor calculated using 100 MinBias events with an average of 400 interactions per bunch crossing with smearing Results optimised for high efficiency: Row window = 2 pixels Column window = 3 pixels @ 1mm, 2mm 4 pixels @ 3mm 6 pixels @ 4mm Mark Pesaresi

13. Algorithm Performance • Sensor separation is again an effective cut on pt – as with the stacked strips • Again, the width of the transition region increases with separation. Due to: • - pixel pitch • - sensor thickness • - charge sharing • track impact point • Efficiencies decrease with sensor separation due to the larger column window cuts – sensor acceptances and fake containment are issues pT discriminating performance of a stacked layer at r=25cm for various sensor separations using 10,000 di-muon events with smearing Cuts optimised for high efficiency: Row window = 2 pixels Column window = 3 pixels @ 1mm, 2mm; 4 pixels @ 3mm; 6 pixels @ 4mm Mark Pesaresi

14. Algorithm Performance Effect of changing window cuts on discrimination curves Efficiencies are unchanged with larger column windows Efficiencies are recovered (at larger separations) when row window is increased but also has the effect of decreasing the pt cut pT discriminating performance of a stacked layer at r=25cm for a sensor separation of 4mm and various algorithm cuts using 10,000 di-muon events with smearing Mark Pesaresi

15. Algorithm Performance Choosing a sensor separation of 2mm, the effect of the window cuts has been determined Efficiency of a 50 GeV muon/antimuon generating a stub in the stacked layer [%] Row Width Column Width 20,000 single 50GeV Muon/Antimuon events with smearing Data rate reduction factor achieved on MinBias events at SLHC pileup Row Width Column Width 100 MinBias events with an average of 400 interactions per bunch crossing with smearing Mark Pesaresi

16. Implications • In order to reduce Lorentz drift, sensors have been tilted – correlation requires that an offset must be programmed in order to match hits from high pt tracks • - At its most basic, a calibration constant must be uploaded for each pixel row on a sensor • - If technology changes, sensors may not need to be tilted • Instead of the correlator performing a difference analysis on two hits, a programmable map between an address on the upper sensor and multiple addresses on the lower sensor would simplify implementation and reduce rate & fakes. Is this possible? • If layer is part of a multi-stack detector, a high efficiency is preferable to large rate reductions. We only need to remove the majority of low pt tracks. Multiple stacks should remove the fakes if combinatorics are not too high. A 2mm separation at 25cm seems reasonable. • To maintain high efficiencies, the column window cut must be kept wide. Can such a column window cut be implemented on detector? Mark Pesaresi

17. Some Numbers • Typical MinBias event at SLHC luminosity: • 1455 tracks > 2 GeV • 4 tracks > 8 GeV • (in region |eta| < 2.14) • Using a stacked pixel layer at 25cm (|eta| < 2.14) with pixel pitch 100μmx2.37mm and 2mm sensor separation [row window=2, column window=3] • 140 stubs • includes 25 fake stubs • includes 20 duplicate stubs • Every event is triggered • A second stacked layer would reduce the number of fakes, the number of tracks (if pt threshold is raised) and allow sufficient resolution for matching to other sub-detectors. Mark Pesaresi

18. Sensor Readout • A method for reading out stacked sensors for hit correlation is required • - Readout and decision every bunch crossing • - Low power G.Hall – July 2008 Mark Pesaresi

19. Sensor Readout • Module divided into 64 blocks of 4 rows per column • Requires minimum 10 address lines: • 6 bit block address • 4 bit pattern • e.g. x000,00x0,0xx0, etc. • Assumes that only 1 block is hit per column – reasonable since <1 pixel hit per column on average Block1 Block0 4 x 100μm 2.37mm Correlator Correlator Mark Pesaresi

20. Sensor Readout Analysis modified to simulate this method of correlation Readout block stubs and pattern data Run original algorithm Correlate hit blocks Sort data into blocks Correlate blocks with pattern data Readout stubs On-detector Off-detector Blocks are correlated in a similar way to before with a block (row) difference and a column difference. As before, an offset is required to match the blocks correctly Cuts can be placed on the window width for both blocks and columns Investigated how well top method worked and the data rate reductions possible Mark Pesaresi

21. Sensor Readout Choosing a sensor separation of 2mm, the effect of block cuts have been determined Results are for block correlation followed by standard algorithm with [row window=2, column window=3] Block Width Efficiency of a 50 GeV muon/antimuon generating a stub in the stacked layer [%] Column Width 20,000 single 50GeV Muon/Antimuon events with smearing Block Width Column Width Data rate reduction factor achieved on MinBias events at SLHC pileup 100 MinBias events with an average of 400 interactions per bunch crossing with smearing Mark Pesaresi

22. Implications Require at least a factor 10 reduction in rate to read out detector. Achievable with a block width cut of 2. For reasonable efficiencies, a column width cut of at least 2 is still required. How can this be performed easily on detector? Offsets are still needed when applying correlation to blocks – can this be implemented on detector? • A small fraction of columns contain more than one hit per BX (in some cases up to 6 hits). Is this important, can it be reduced or ignored? • Largest cause is due to hits on block boundaries • e.g. |0000|000x|x000|0000| Mark Pesaresi

23. Correlator Alternative Readout Designs Instead of reading out column-wise each row or block, could we read out row-wise? Occupancy must be low enough (or module must be small). Is this an issue? Removes need for large column window comparisons Cut could be sufficient for cutting data rate off detector by x10 If column address is kept, further processing off-detector should cut this down and remove fakes due to pileup Mark Pesaresi

24. Extra Stacked Layers • A stacked strip layer could be constructed • Feasibility of using stacked strip layers in the outer tracker was investigated earlier this year • - high pt stubs were generated 1.0 mm Separation 1.5 mm Separation 2.0 mm Separation 2.5 mm Separation 3.0 mm Separation 4.0 mm Separation 5.0 mm Separation Aim is to add this layer to the geometry and investigate possibility of correlating stubs in each layer Mark Pesaresi

25. Summary • Strawman B has been used as the basis to commence trigger studies using a stacked pixel layer at 25cm • Algorithm to correlate digi hits from high pt tracks has been written • Performance of algorithm in ideal conditions measured - ~95% maximum efficiency of detecting high pt tracks, ~ x100 reduction in data rate • Next step will be to correlate stubs from this layer to those from a layer further out – such as a stacked strip layer at large radius or a stacked pixel layer at mid radius • Will be possible to then estimate pt resolutions, trigger rates etc • Possible methods of reading out sensor data have been looked at • Block correlation is successful but needs some refining • Other methods still need analysing • Still plenty to investigate… • Effect of occupancy on performance • Effect of changing layer radii • Effect of changing pixel pitch, short/long pixel strips • Possibility to extend layers to high eta • … Mark Pesaresi

26. Backup • Fast Sim gives an average occupancy of 0.05% (up to 0.15% instantaneous) at an average of 400 interactions per event for a layer at 25cm extending to |eta| < 2.14 • Assume Full Sim will give x3 occupancy • 0.15% x 17,448,960 channels = 26,173 hit channels • 30k channels require 2813, 2.56Gbps links assuming 12bit address per channel at 20MHz • Link Power: 5.6 kW (322uW/ch) – Geoff suggests a budget of 300μW/ch for the pt layers • Cutting the number of channels to readout by x10 using hit correlation brings link power for the pt layers down to reasonable values • Total Hits • Block Stubs • Pixel Stubs Mark Pesaresi

27. Backup Layer Occupancy (No. digi hits in layer / total channels in layer) Module Occupancy (No. digi hits in occupied module / total channels in module) Note: Full Sim occupancies estimated at 3x these values 100 MinBias events, ~400 interactions per bx with smearing Mark Pesaresi

28. Backup Pixel block statistics < 6 100 MinBias events, ~400 interactions per bx with smearing using block width=2, column width=3, row width=2, column width=3 Mark Pesaresi

29. Backup Max Efficiency vs Eta Does not take pileup into account 20,000 single 50 GeV muon/antimuon events using block width=2, column width=3, row width=2, column width=3 Mark Pesaresi

30. Backup Number of pixel hits per block Effect of placing track width cut in each block No cut >2 Consecutive Hits >1 Consecutive Hit Effect is small 400 MinBias pileup (50 um sensor) Mark Pesaresi