Speaker
Description
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 segments 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 novel ML architectures like Graph Neural Networks and Transformers to embed segments in a learned latent space and use object condensation to assemble full tracks in a single inference step. This segment-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, this approach achieves high reconstruction efficiency with low fake and duplicate rates, demonstrating the promise of advanced ML architectures for next-generation particle tracking.