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

arXiv: https://arxiv.org/abs/2610.15963 · HTML: https://mlsanity.com/paper/2610.15963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking](https://mlsanity.com/paper/2103.06995/) (2021)
- [Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data](https://mlsanity.com/paper/2002.03605/) (2020)
- [High Pileup Particle Tracking with Object Condensation](https://mlsanity.com/paper/2312.03823/) (2023)
- [Alpaka - An Abstraction Library for Parallel Kernel Acceleration](https://mlsanity.com/paper/1602.08477/) (2016)

## Abstract (arXiv)

High-pileup conditions in CMS during the HL-LHC era make charged-particle tracking increasingly challenging as detector occupancy and combinatorics grow. 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. 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. 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. This object-based representation reduces complexity in dense environments and makes the model less sensitive to ambiguities introduced by high pileup. Evaluated on CMS Phase-2 simulation of tbart 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.