A Language Model for Particle Tracking

arXiv:2402.10239v1 Announce Type: cross
Abstract: Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning model for one task with supervised learning techniques. The trained models work well for tasks they are trained on but show no or little generalization capabilities. We propose to unify these models with a language model. In …

abstract analysis arxiv cs.lg current deep learning design hep-ex hep-ph language language model physics practice supervised learning tasks tracking train type