Hierarchical clustering in particle physics through reinforcement learning
Nov 16, 2020
Citations per year
Abstract: (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.Note:
- Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020
- hierarchy
- Monte Carlo
- Markov chain
- cluster
- neural network
- parton
- stochastic
- mathematical methods
References(22)
Figures(3)
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