# HyperTrack: Neural Combinatorics for High Energy Physics

arXiv: https://arxiv.org/abs/2309.14113 · HTML: https://mlsanity.com/paper/2309.14113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Abstract (arXiv)

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.