Corpus ID: 226975720

Hierarchical clustering in particle physics through reinforcement learning

  title={Hierarchical clustering in particle physics through reinforcement learning},
  author={J. Brehmer and S. Macaluso and D. Pappadopulo and K. Cranmer},
  • J. Brehmer, S. Macaluso, +1 author K. Cranmer
  • Published 2020
  • Computer Science, Physics
  • ArXiv
  • 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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