Long lived particle searches with the
CMS experiment. Present and future
S. Folgueras with input from M. Alcaide, A. Escalante, P. Martínez Ruiz
del Árbol, R. López, and A. Soto.
           My (BSM)
        Where       research
              to look        interests
                       for new   physics?
        Improve precision of SM tests (i.e. Higgs couplings, 𝑚! )
        Target unobserved SM processes (i.e. 𝐻 → 𝐻𝐻; 𝐻 → 𝑐𝑐)
      Search for deviations at high momenta (i.e. Effective Field Theories)
                  1. Improve the precision of SM tests (e.g Higgs couplings, MW)
esearch interests
      Probe   new2. phase
                      Target unobserved
                          space          SM processes
                                 (i.e. Long-lived      (e.g 4 top, H → cc, H → HH)
                                                  particles)
       Coupling modifier measurements
                     3. inSearch
       and their evolution time   for deviations at high-p T
                                                             (e.g SM as EFT)
                                                     CMS-EXO-19-019
                       4.     Probe new phase space (e.g Long-lived particles)
 the precision of SM tests (e.g Higgs couplings, MW)
nobserved SM processes (e.g 4 top, H → cc, H → HH)
or deviations at high-pT (e.g SM as EFT)
                                                                                     Bulk of searches
         Nature 607, 60–68 (2022)
         19Escalante @ICTEA Seminar
   From A.                                                                                              2
Long-lived particles appear everywhere
    Why long-lived particles?
   And their c𝜏 was critical to the design of different experiments in HEP
                                                                  • The SM is full of LLPs:
                                                                  ● • Kaon
                                                                      muonphysics  (e.g NA62)
                                                                           (𝜏 = 2.2𝜇𝑠)
                                                                            c𝜏(K+)==3.71
                                                                       ○ (c𝜏(K+)
                                                                    • Kaon           3.71mm
                                                                      •   Heavy flavour
                                                                  ●   Heavy
                                                                      • c𝜏(D+)flavor   physics
                                                                                = 311.78 𝜇m     (e.g LHCb)
                                                                                   +
                                                                              c𝜏(D
                                                                      • ○c𝜏(B+)      ) = 311.78
                                                                                = 491.06  𝜇m 𝜇m
                                                                                   +
                                                                        ○     c𝜏(B  ) = 491.06
                                                                  • There is no reason           𝜇m they won’t be present on
                                                                                           to believe
                                                                      BSM theories.
                                                                  ●       Higgs physics (e.g ATLAS/CMS)
                                                                            ○ 𝜏H < 1.9·10-13 s, (𝜏H= 2· 10-22 s in SM)
                                                                            ○ c𝜏(𝜏) = 87.03 𝜇m (e.g H → 𝜏𝜏)
      arXiv:2212.03883
 From A. Escalante @ICTEA Seminar                                                                                              3
                     G. Cottin
                     @LHCP 2023
arXiv:2212.03883v1
                                  4
  Experimental signatures of long-lived particles
https://doi.org/10.1098/rsta.2019.0047
                                                    5
Run 3
• After the short data taking year in 2023, LHC is performing well
• 85.5 fb-1 recorded in CMS in 2022+2023+2024 at 13.6 TeV
• Improved trigger strategy!
                                                                     6
  First Run 3 result: Displaced dimuons at 13.6 TeV
       https://cms.cern/news/long-lived-particles-light-lhc-run-3-data
       https://home.cern/news/news/physics/cms-collaboration-
       cern-presents-its-latest-search-new-exotic-particles
From A. Escalante @ICTEA Seminar
                                                                                                            JHEP 05 (2024) 047
Displaced dimuons at 13.6 TeV. New triggers
• Use the 2022 dataset (36.7 fb-1) recorded with new LLP triggers with thresholds down to pT(𝜇) > 10 GeV
  •   Re-optimized L1 triggers, including pT without beam spot constraint, and new reconstruction algorithms.
  •   Use dxy information at trigger level to control the background rate.
• Factor 2-4 more signal efficiency
                                                                                                                             8
                                                                                          JHEP 05 (2024) 047
  Displaced dimuons at 13.6 TeV
  Despite 2.5 smaller dataset, comparable (or better) sensitivity w.r.t. 13 TeV result.
From A. Escalante @ICTEA Seminar                                                                           9
                                                                                                              CMS-EXO-23-013
Displaced jets at 13.6 TeV
Despite 2.5 smaller dataset, up to factor 10 improvement.
                                                   • Improved trigger strategy: 4-17 times larger signal acceptance
                                                   • Improved analysis strategy, using GNNs as taggers to identify
                                                     dijets arising from LLP decays
                                                     •   Using displaced track features and info from the displaced vertex
                                                     •   the displaced tagger achieves a background rejection factor of 104
                                                         when the signal efficiency is ~55%
                                                                                                                              10
                                                             [CERN-LPCC-2019-
                                                                          01]
Towards the HL-LHC
• Preparing for the big upgrade of the LHC detectors,
  starting 2029.
• HL-LHC upgrade offers an unprecedented opportunity to
  explore uncharted lands and achieve scientific progress.
• 10 times more data to what we will have by the end of
  Run 3 will facilitate a rich physics program.
• Extend reach of new physics searches: unexplored
  signatures (LLPs, HSCPs… ) or regions of the phase-
  space will be within reach.
• Improve current understanding of the SM and Higgs
  sector by improving existing precision measurements
  and accessing rare decays (H → 𝜇𝜇) or production modes
  (HH) previously unseen at the LHC.
• However, this physics program will have to overcome
  significant challenges to succeed.
                                                                           11
Improve muon triggers using the existing architecture
              hits
             tracks
                                                        Displaced
                                                        signatures
14/06/2023                                                           12
  Extending LLPs coverage (yet in Run-3)
From A. Escalante @ICTEA Seminar           13
Reconstruction of muon showers
                     x10
Mixing angle
                            Current trigger (missing
                            energy-based) strategy
                            Hit-counting trigger
                            strategy
                JHEP 02 (2023) 011; arXiv:2210.17446
14/06/2023                                             14
Graph Neural Networks for real-time muon reconstruction
14/06/2023                                                15
Graph Neural Networks for particle reconstruction
                                                                                                                            F. Siklér, “Combination of various data analysis techniques for efficient
                         Representing tracking data using graphs                                                            track reconstruction in very high multiplicity events”,
                                                                                                                            Connecting the Dots conference 2017 (link)
                                                                                                                            S. Farrell et al., “Novel deep learning methods for track reconstruction”,
                                                                                                                            proceedings of Connecting the Dots conference 2018 (link)
               Charged particles leave hits in the              Represent the data using a                           Goal:
               detector                                         graph                                                classify the edges of the graph
                                                                                                                                                         •     High classification
                                                                                                                                                               score
                                                                                                                                                         •     => high probability
                                                                                                                                                               that the edge is part of
                                                                                                                                                               a track
                                                                                                                                                         •     Low classification score
                                                                                                                                                         •     => low probability that
                                                                                                                                                               the edge is part of a
                                                                                                                                                               track
                                                       One node of the graph = one hit in the detector
                                                       Connect two nodes using an edge
                                                       if “it seems possible” that the two hits
                                                       are two (consecutive) hits on a track
             Jan Stark                         European AI for Fundamental Physics Conference, Amsterdam | April/May 2024                                                          5
14/06/2023                                                                                                                                                                                               16
Explore capabilities of AI-engines
Provide the necessary throughput and latency for triggering?
                      x8
                 performance
                     x5
                 throughput
14/06/2023                                                     17
Thanks for listening!
       Foreseen
   improvements on
  detection efficiency
 and triggering might
allow the discovery of
     BSM physics.
 Provide an answer to
     fundamental
 questions of nature.
14/06/2023               18
backup
         19
   Our demonstrator
ICTEA 2024 A. ZABI
                                                                                            52
                                                                      CMS L1 TRIGGER @ HL-LHC
TESTING AND SYSTEM DEMONSTRATION
 Phase-2 Level-1 Trigger system demonstration
 ‣ Single-board and multiple board tests performed
 ‣ Integration centers across the globe: larger scale
   integration @ CERN (904). Multiple flavour board
   tests.
 ‣ Slice test in Muon Barrel Trigger during Run-3.
   Installation @P5: DT—>BMT—>GMT—>GT
 ‣ Board interconnection: protocol
     ‣ Links (asynchronous) operation @ 25.78 Gb/s
     ‣ L1 Trigger boards sending packets only once
       (no retransmission) → error proof
     ‣ Protocols (64/66b or 64/67b) encoding
       achieved low error rate, validated recovery
       mechanism etc.                                   Building 904 @ CERN
                        Muon Trigger Slice Test
                                                                                                 20
INTREPID
                         FW R&D                                Slice Test
                      Timing, latency                        System-level
       01
       M1             and occupancy          M3
                                             03              demonstrator            05
                                                                                     M5
                      Demonstrator                          Proof of concept
     SW R&D                                  GNNs                                 Discovery
  Displaced jets in
     the barrel           M2
                          02            Hit-based pattern
                                            recognition          M4
                                                                 04            Long-lived particles
                                                                                at the HL-LHC or
                                                                                     beyond
    Kalman filter                       AI engines with
                                        Versal ACAPs                              Dark matter?
HL-LHC: challenges
• Expected pileup (PU): ~140 (nominal HL-LHC lumi)                • Radiation damage / accumulated dose in detectors
                                                                    and on-board electronics may result in a progressive
• Motivates/requires:
                                                                    degradation of the performance.
  •   Improved granularity wherever possible
  •   Novel approaches to in-time Pile Up mitigation: Precision   • Maintain detector performance in harsh conditions:
      Timing detectors (30ps)                                       •   The complete replacement of the Tracker and Endcap
  •   A complete renovation of the Trigger and DAQ systems              Calorimeter systems.
      for better selectiveness, despite the high PU.                •   Major electronics overhaul and consolidation of the
                                                                        Barrel Calorimeters and Muon systems
                                                                                                                              22
                                                                                                                 Refined version using module triplets:
                                                                                                                 C. Rougier, PhD thesis, Université de Toulouse,
                                                                                                                 defended September 2023 (link)
   Graph building techniques
            New data-driven graph construction method:
                • build graphs starting from a list of possible connections from a zone to another zone: the module map
                • done using 90k simulated tt events at <!> = 200, considering particles with pT > 1 GeV and leaving at least 3 hits
Jan Stark                           European AI for Fundamental Physics Conference, Amsterdam | April/May 2024                                                     8
   14/06/2023                                                                                                                                                          23