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10-15 March 2019
Steinmatte conference center
Europe/Zurich timezone

Reinforced Jet Grooming

12 Mar 2019, 18:40
Steinmatte Room A

Steinmatte Room A

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


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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