论文标题
通过增强学习在粒子物理中的分层聚类
Hierarchical clustering in particle physics through reinforcement learning
论文作者
论文摘要
粒子物理实验通常需要通过观察到的最终晶体的分层聚类重建衰减模式。我们表明，可以将此任务用作马尔可夫决策过程，并调整强化学习算法来解决它。特别是，我们表明，以神经政策为指导的蒙特卡洛树搜索可以构建高质量的层次聚类，并且表现优于建立的贪婪和梁搜索基线。
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.
