Apr 15 – 18, 2019
Europe/Zurich timezone
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GroomRL: jet grooming through reinforcement learning

Apr 17, 2019, 4:30 PM
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium


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Frederic Alexandre Dreyer (Oxford)


We introduce a novel implementation of a reinforcement learning algorithm which is adapted to the problem of jet grooming, a crucial component of jet physics at hadron colliders. We show that the grooming policies trained using a Deep Q-Network model outperform state-of-the-art tools used at the LHC such as Recursive Soft Drop, allowing for improved resolution of the mass of boosted objects. The algorithm learns how to optimally remove soft wide-angle radiation, allowing for a modular jet grooming tool that can be applied in a wide range of contexts.

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