Jet grooming through reinforcement learning(15'+5')

Jul 23, 2019, 4:40 PM
20m
32-123 (MIT)

32-123

MIT

https://goo.gl/maps/Wx14Gpe2wRy

Speaker

Stefano Carrazza (CERN)

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

Presentation materials