You are currently offline. Some features of the site may not work correctly.

Corpus ID: 226975720

Hierarchical clustering in particle physics through reinforcement learning

@article{Brehmer2020HierarchicalCI,
title={Hierarchical clustering in particle physics through reinforcement learning},
author={J. Brehmer and S. Macaluso and D. Pappadopulo and K. Cranmer},
journal={ArXiv},
year={2020},
volume={abs/2011.08191}
}

Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles. We show that this task can be phrased as a Markov Decision Process and adapt reinforcement learning algorithms to solve it. In particular, we show that Monte-Carlo Tree Search guided by a neural policy can construct high-quality hierarchical clusterings and outperform established greedy and beam search baselines.