Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS

Key points

• High-pileup conditions in CMS during the HL-LHC era make charged-particle tracking increasingly challenging as detector occupancy and combinatorics grow.1
• The LST objects, namely the triplets used in this study, are built according to geometry- and physics-driven criteria and carry richer local structure, geometrical cues, and physics quantities such as transverse momentum, which helps the model incorporate detector-level information from the start.1
• We apply a Transformer encoder with geometrically masked sparse self-attention to embed these objects in a learned latent space, and use object condensation to assemble full tracks in a single inference step.1
• Evaluated on CMS Phase-2 simulation of $t\bar{t}$ events at $\langle μ\rangle = 200$, the approach achieves reconstruction efficiency comparable to the LST quintuplet baseline while reducing the fake rate by roughly a factor of three and suppressing duplicates almost entirely, demonstrating the promise of advanced ML architectures for next-generation particle tracking.1

Sources (1)

• [1]Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 10:34 PM

  We present a hybrid approach that exploits Line Segment Tracking (LST) objects rather than individual hits to enable the first CMS ML-based track reconstruction algorithm.

  High-pileup conditions in CMS during the HL-LHC era make charged-particle tracking increasingly challenging as detector occupancy and combinatorics grow.

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