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.

Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020

References in corpus (3)

• The NumPy array: a structure for efficient numerical computation
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Jet grooming through reinforcement learning

Cited by in corpus (2)

• Reframing Jet Physics with New Computational Methods
• Exact and Approximate Hierarchical Clustering Using A*