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.
Cite
@article{arxiv.1910.00410,
title = {libGroomRL: Reinforcement Learning for Jets},
author = {Stefano Carrazza and Frédéric A. Dreyer},
journal= {arXiv preprint arXiv:1910.00410},
year = {2019}
}
Comments
6 pages, 3 figures, conference proceedings of ACAT and ICML. arXiv admin note: substantial text overlap with arXiv:1903.09644