Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments

TL;DR

This paper demonstrates a proof-of-concept for using human-in-the-loop reinforcement learning to automate data quality monitoring in particle physics, reducing human bias and improving accuracy.

Contribution

It introduces a multi-agent RL system with human intervention for continuous monitoring, incorporating data augmentation to handle scarce data and adapt to changing conditions.

Findings

Reduced bias in human classification

Improved accuracy over baseline

Effective data augmentation techniques

Abstract

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.