Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments
This work addresses the costly and limited accuracy of manual data monitoring in particle physics experiments, though it is incremental as it builds on existing RL methods and is validated only on synthetic data.
The paper tackles the problem of automating Data Quality Monitoring in particle physics experiments, which is currently done manually and is costly and inaccurate, by proposing a human-in-the-loop Reinforcement Learning approach that reduces noise in human classification and improves accuracy over the baseline.
Data Quality Monitoring (DQM) is a crucial task in large particle physics experiments, since detector malfunctioning can compromise the data. DQM is currently performed by human shifters, which is costly and results in limited accuracy. In this work, we provide a proof-of-concept for applying human-in-the-loop Reinforcement Learning (RL) to automate the DQM process while adapting to operating conditions that change over time. We implement a prototype based on the Proximal Policy Optimization (PPO) algorithm and validate it on a simplified synthetic dataset. We demonstrate how a multi-agent system can be trained for continuous automated monitoring during data collection, with human intervention actively requested only when relevant. We show that random, unbiased noise in human classification can be reduced, leading to an improved accuracy over the baseline. Additionally, we propose data augmentation techniques to deal with scarce data and to accelerate the learning process. Finally, we discuss further steps needed to implement the approach in the real world, including protocols for periodic control of the algorithm's outputs.