Hierarchical clustering in particle physics through reinforcement learning

Understand

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.