# Hierarchical clustering in particle physics through reinforcement learning

@article{Brehmer2020HierarchicalCI, title={Hierarchical clustering in particle physics through reinforcement learning}, author={Johann Brehmer and Sebastian Macaluso and Duccio Pappadopulo and Kyle Cranmer}, journal={ArXiv}, year={2020}, volume={abs/2011.08191} }

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.

## 3 Citations

A Living Review of Machine Learning for Particle Physics

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This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

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A new approach based on A* search is introduced that overcome the prohibitively large search space by combining A* with a novel trellis data structure and provides significantly improved theoretical bounds on the time and space complexity of A* for clustering.

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- PhysicsEPJ Web of Conferences
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We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient…

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