Jet grooming through reinforcement learning
This provides a modular grooming technique for particle physics experiments, but it is incremental as it matches rather than surpasses existing methods.
The paper tackled the problem of optimizing jet grooming strategies for collider experiments by introducing a reinforcement learning algorithm, resulting in a method that matches state-of-the-art techniques and improves mass resolution for boosted objects.
We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments. The RL agent is trained with a reward function constructed to optimize the resulting 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. These results are accessible through the corresponding GroomRL framework.