HyperTrack: Neural Combinatorics for High Energy Physics
This addresses algorithmic challenges in high energy physics for applications like particle tracking and jet physics, but appears incremental as it combines existing techniques like graph neural networks and transformers.
The paper tackles combinatorial inverse problems in high energy physics by introducing a deep learning clustering algorithm, achieving effectiveness in particle tracking simulations.
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.