Using Line Segment Tracking to enable ML-based Track Reconstruction in CMS
The research introduced a track-reconstruction model using Transformers by treating Line Segment Tracking (LST) objects as tokens. LST uses features from silicon sensors to build increasingly complex objects, culminating in Triplets. The model is trained with an object condensation objective and performs better than the LST baseline, demonstrating that physics-informed models can match or exceed combinatorial algorithms under high pileup conditions.