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Hierarchical clustering in particle physics through reinforcement learning

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 نشر من قبل Johann Brehmer Mr
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
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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.



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