Hierarchical clustering in particle physics through reinforcement learning
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2020
- DOI:
- 10.48550/arXiv.2011.08191
- arXiv:
- arXiv:2011.08191
- Bibcode:
- 2020arXiv201108191B
- Keywords:
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- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- High Energy Physics - Phenomenology
- E-Print:
- Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020