# Hierarchical clustering in particle physics through reinforcement learning

arXiv: https://arxiv.org/abs/2011.08191 · HTML: https://mlsanity.com/paper/2011.08191/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics](https://mlsanity.com/paper/2308.16680/) (2023)
- [Automating DBSCAN via Deep Reinforcement Learning](https://mlsanity.com/paper/2208.04537/) (2022)
- [Exact and Approximate Hierarchical Clustering Using A*](https://mlsanity.com/paper/2104.07061/) (2021)
- [Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2505.04339/) (2025)
- [Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation](https://mlsanity.com/paper/2610.04602/) (2026)

## Abstract (arXiv)

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.