Combinatorial inverse problems in high energy physics span enormous algorithmic challenges. This work presents a new deep learning driven clustering algorithm that utilizes a space-time non-local trainable graph constructor, a graph neural network, and a set transformer. The model is trained with loss functions at the graph node, edge and object level, including contrastive learning and meta-supervision. The algorithm can be applied to problems such as charged particle tracking, calorimetry, pile-up discrimination, jet physics, and beyond. We showcase the effectiveness of this cutting-edge AI approach through particle tracking simulations. The code is available online.
@article{arxiv.2309.14113,
title = {HyperTrack: Neural Combinatorics for High Energy Physics},
author = {Mikael Mieskolainen},
journal= {arXiv preprint arXiv:2309.14113},
year = {2023}
}