Muon trigger upgrades in the CMS experiment for the HL-LHC
ALEJANDRO SOTO RODRÍGUEZ (ON BEHALF OF THE CMS COLLABORATION)
XIII CPAN days (Huelva) 21-23 March 2022
FPU20/02225 funded by Grant PID2020-113341RB-100 funded by
Content
• Introduction.
• The Level 1 muon trigger system for the Phase 2 upgrade at CMS.
• Local trigger reconstruction.
• DT+RPC.
• Muon track finders.
• Overlap muon track finder.
• Global muon trigger.
• Summary.
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Focusing on Spanish contributions
Introduction
• Why the HL-LHC? New-physics processes might be “hidden” in corners of the phase space, or in tiny deviations from the SM.
• A huge data sample (x30 bigger than our current data).
• An open door to “unexplored land”: avoiding unavoidable blind spots due to existing trigger limitations: low momenta, displaced particles.
• Challenges for the muon trigger:
• High luminosity (7.5 ⋅ 1034 cm−2s−1) more simultaneous collisions →output rate and bandwidth increase.
• Maintain trigger 𝑝𝑇 thresholds while keeping rate under control.
• Deal with the aging of the detectors in high radiation environment.
• To overcome these challenges, the muon and trigger systems are being upgraded:
• Full replacement of the DT chambers electronics → FPGAs.
• Increase 𝜂 coverage adding the GEM, iRPC and ME0 subdetectors.
• For the first time, tracker tracks will be available at L1 → better 𝑝𝑇 resolution.
• Update and development of new algorithms to:
• Maintain and improve the efficiency.
The CMS Muon Detector
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Barrel Overlap
En dc ap
• DT
• 𝜂 < 1.2.
• Precise spatial resolution (250 𝜇m).
• RPC/iRPC
• 𝜂 < 1.9 (RPC).
• 1.9 < 𝜂 < 2.5 (iRPC).
• Great time resolution (1.5 ns).
• CSC
• 0.9 < 𝜂 < 2.4
• Precise spatial resolution (150 𝜇m).
• Robust against large background.
• GEM
• 1.6 < 𝜂 < 2.5
• Robust against large background.
Tracker tracks will also be available at L1
The Phase-2 L1muon trigger
Barrel Layer-1:
Local information, reconstructs segment direction in each station
→ Trigger Primitives
(TPs).
GMT (correlator):
Combines muon and track information to
identify tracks as muons and to improve muon momentum estimation using the tracker info.
Muon Track Finders:
Combines local info from different stations to find the trajectory and
estimate momentum.
Local trigger reconstruction
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DT+RPC local reconstruction
• The new backend system will achieve resolutions for the TPs parameters (collision time, position and direction) comparable to offline reconstruction.
• Start from individual hits and look for straight patterns.
• Two proposals for DT TP generation: analytical method and pseudo-Bayes.
• Analytical method (AM):
• Inputs: wire numbers and hit times with respect to the start of the LHC orbit.
• Three steps:
• Grouping: for a given hypothesis of muon trajectory within a SL, combinations of at least 3 cells are delivered.
• Fitting:TP time and track parameters are computed using exact formulas from a 𝜒2 minimization.
• Correlation: matching segments within a ±25 𝑛𝑠 window using the 2 𝑟 − 𝜙 SLs. Parameters are then updated.
• The combination with information from the RPC improves timing resolution
→ super-primitives.
DT+RPC local reconstruction
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• Analytical method (AM):
• Implemented as a software C++ emulator and in firmware (currently being tested at CIEMAT on a Virtex 7 FPGA)
→ good agreement observed between them.
• The AM efficiency and resolutions have been evaluated using samples simulating the LHC phase-2 conditions.
• Efficiencies are found to be very close to 1, with RPC helping in recovering performance when ageing is switched on.
DT+RPC local reconstruction
• Analytical method (AM):
• During the second LHC long shutdown, four DT chambers (MB1 to MB4 of the DT sector 12 of wheel +2) have been instrumented with Phase 2 on-board DT electronics (OBDT) to setup a demonstrator of the upgraded system, called DT Slice Test.
• Good agreement between FW and emulator.
• Great time resolution ~ 2 ns.
DT+RPC local reconstruction
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• Pseudo-Bayes.
• Merging of grouping and correlation steps.
• It simultaneously consider hits from SL1 and SL3 using a set of pre-computed patterns.
• Higher resilience against aging.
• Reduce noise from multiple candidates.
• Less combinatorics of hits to test.
• Patterns are then passed to the fitting step.
Comparison against standard AM
Work in progress
Muon Track
Finders
OMTF: Naïve-Bayes Classifier
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• Challenges in the overlap region:
• Combines information from 3 subdetectors: DTs, RPCs and CSCs.
• Complicated geometry and difficult magnetic field.
• The first muon station (MB1), which is the most important for the standalone 𝑝𝑇 measurement, will be highly affected by aging.
• Reconstruction based on a Naïve-Bayes classifier:
• It performs muon identification and 𝑝𝑇 measurement in one step.
• It is assumed that the log-likelihood that a muon has a given 𝑝𝑇 𝑝(𝑝𝑇|hits) is just a sum of the log-likelihoods of the muon hit 𝜙 positions in each detector layer
𝑝𝑙𝑎𝑦𝑒𝑟(𝑝𝑇|𝜙𝑑𝑖𝑠𝑡).
• Compare with precomputed patterns → pattern with the biggest likelihood gives the 𝑝𝑇 estimation.
OMTF: Naïve-Bayes Classifier
• Efficiency over 95% for 𝑝𝑇 ≥ 20GeV.
• Decrease of 5% in efficiency for the worst-case ageing scenario: non affected thanks to the redundancy of the system.
• Rates scale linearly with pileup.
CMS-TDR-021
Displaced OMTF
• A new approach is proposed to reconstruct displaced muons.
• The algorithm is modified to give two values for the muon 𝑝𝑇: constrained and unconstrained to the interaction point.
• Same patterns are used for prompt and displaced muons.
• Nearly 50% efficiency for displaced muon with an impact parameter up to 150 cm.
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Work in progress
Global Muon Trigger
• Change in paradigm: tracker tracks available at L1.
• GMT will be able to perform a global muon reconstruction.
• Great 𝑝𝑇 resolution → will allow to lower the thresholds a lot.
• Input information:
• Tracker tracks.
• Standalone tracks and stubs.
CMS-TDR-021
Tracker track
Muon stubs
• Output information:
• Tracker tracks matched to standalone muons.
• Tracker tracks matched to muon stubs.
Summary
• Extraordinary new capabilities available for L1 muon triggers in HL-LHC:
• Higher bandwidth and latency.
• Triggering in unique signatures will be possible already at L1.
• L1 trigger primitive generation will significantly extend Phase-1 capabilities thanks to the use of FPGAs and the improved algorithms.
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• New muon track finder algorithms can:
• Keep efficiencies high while rates are under control.
• Reconstruct highly displaced muons.
• Some of these algorithms will be tested during Run 3.
• Exciting times ahead with the Run 3 and the preparation for the HL-LHC .
Thanks for your attention!
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Backup
CSC+GEM+(iRPC) local reconstruction
• New detectors will be installed for the HL-LHC.
• GEM and CSC hits are received through fiber. TPs are built combining GEM and CSC hits
• Local trigger efficiency improved thanks to redundancy.
• GEM-CSC bending angle could help control trigger rate by cutting out low 𝑝𝑇 muons at EMTF level.
• Integration of (i)RPC key to reduce rate and allow for HSCP triggering.
• Firmware development has been demonstrated.
CMS-TDR-021
Muon Track Finders (MTF)
• Information from the previous layer (TPs) is used to try to run pattern recognition algorithms across all muon chambers.
• Magnetic field, multiple scattering…
• Different regions of the detector present different challenges:
• Three distinct approaches: Barrel, Overlap and Endcap.
• Phase-2 design will extend greatly the capabilities of the MTFs:
• (displaced) Stand-alone muons.
• Correlation with tracker tracks information will allow to:
• Tracker+muon stubs / track+muons.
• Heavy Stable Charged Particles (HSCP).
• Some of these algorithms shown here might be already tested during Run 3.
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BMTF:
𝜂 < 0.82OMTF: 0.82 < 𝜂 < 1.24
EMTF:
𝜂 > 1.24BMTF: Kalman filter
• Reconstruction based on a Kalman filter:
• Used successfully in Run 2 offline reconstruction → an optimised version for L1 is implemented (KBMTF).
• Propagate inwards, begins with seeding from the outermost muon detector.
• For each hit estimation of: 𝑘 (curvature), 𝜙 (position) and 𝜙b (bending).
• Update parameters and update until last DT station.
• Rate approximately scales linearly with PU.
Provides both vertex constrained and unconstrained measurements
CMS-TDR-021
EMTF: Neural Networks
• Phase-1 algorithm rate scale non-linearly with PU → new strategy (EMTF++).
• Incorporate new muon detectors: better efficiency, timing and momentum assignment.
• Reconstruction based on Deep Neural Network (DNN):
• Pattern recognition techniques to find TPs compatible with muon trajectories.
• Angular position (𝜙 and 𝜃), bending, time and quality used as input to NN.
• DNN estimates the most likely 𝑝𝑇 of the muon.
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