libGroomRL: Reinforcement Learning for Jets

Abstract

In these proceedings, we present a library allowing for straightforward calls in C++ to jet grooming algorithms trained with deep reinforcement learning. The RL agent is trained with a reward function constructed to optimize the groomed jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. The neural network trained with GroomRL can be used in a FastJet analysis through the libGroomRL C++ library.

arXiv comment6 pages, 3 figures, conference proceedings of ACAT and ICML. arXiv admin note: substantial text overlap with arXiv:1903.09644

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• Playing Atari with Deep Reinforcement Learning
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• A measurement of the soft-drop jet mass in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Measurements of the differential jet cross section as a function of the jet mass in dijet events from proton-proton collisions at $\sqrt{s}=$ 13 TeV