PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction

arXiv:2402.11538v1 Announce Type: cross
Abstract: In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. …

abstract arxiv augmentation collision cs.lg energy events format hep-ph hierarchical physics products space tree type