Speaker
Dr
Stefano Carrazza
(University of Milan)
Description
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.
Primary authors
Frederic Dreyer
(MIT)
Dr
Stefano Carrazza
(University of Milan)