https://doi.org/10.1051/epjconf/202429509021
HyperTrack: Neural Combinatorics for High Energy Physics
1 High Energy Physics, Blackett Laboratory, Imperial College London, SW7 2AZ, United Kingdom
2 I-X, Imperial College London, W12 0BZ, United Kingdom
* e-mail: m.mieskolainen@imperial.ac.uk
Published online: 6 May 2024
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
