Hierarchical clustering in particle physics through reinforcement learning
Artificial Intelligence
2020-12-21 v2 Machine Learning
High Energy Physics - Phenomenology
Abstract
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.
Cite
@article{arxiv.2011.08191,
title = {Hierarchical clustering in particle physics through reinforcement learning},
author = {Johann Brehmer and Sebastian Macaluso and Duccio Pappadopulo and Kyle Cranmer},
journal= {arXiv preprint arXiv:2011.08191},
year = {2020}
}
Comments
Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020