In these proceedings, we present a library allowing for straightforward calls in C++ to jet grooming algorithms trained with deep reinforcement learning. The RL agent is trained with a reward function constructed to optimize the groomed jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. The neural network trained with GroomRL can be used in a FastJet analysis through the libGroomRL C++ library.
libGroomRL: Reinforcement Learning for Jets / S. Carrazza, F.A. Dreyer. - (2019 Sep 15). ((Intervento presentato al convegno ICML tenutosi a Long Beach nel 2019.
|Titolo:||libGroomRL: Reinforcement Learning for Jets|
|Settore Scientifico Disciplinare:||Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici|
|Data di pubblicazione:||2019-09-15|
|Appare nelle tipologie:||24 - Pre-print|