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Talk21 min

GroomRL: jet grooming through reinforcement learning

Frederic Alexandre Dreyer · CERN

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About

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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The talk

  1. GroomRL: jet grooming through reinforcement learning 2019-04-17 · 21 min · CERN's player

Credits and licence

Institution
CERN
Department
LPCC Workshops
Speaker
Frederic Alexandre Dreyer
Year
2019
Language
English
Subjects
Computer science
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
Who can watch
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