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}
}

J. Brehmer, S. Macaluso, +1 author K. Cranmer

Published 2020

Computer Science, Physics

ArXiv

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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.