Hierarchical clustering in particle physics through reinforcement learning

Johann Brehmer; Sebastian Macaluso; Duccio Pappadopulo; Kyle Cranmer

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arXiv · 2011.08191

Hierarchical clustering in particle physics through reinforcement learning

Johann Brehmer·Sebastian Macaluso·Duccio Pappadopulo·Kyle Cranmer

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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.

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Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer. 2020-11-16. Hierarchical clustering in particle physics through reinforcement learning. https://arxiv.org/abs/2011.08191

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