Hierarchical clustering in particle physics through reinforcement learning
This addresses the need for more accurate hierarchical clustering in particle physics experiments, though it is an incremental improvement over existing methods.
The paper tackled the problem of reconstructing decay patterns in particle physics by formulating hierarchical clustering as a Markov Decision Process and applying reinforcement learning, resulting in Monte-Carlo Tree Search with a neural policy outperforming greedy and beam search baselines.
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.