Machine Learning based tool for CMS RPC currents quality monitoring

TL;DR

This paper presents an automated machine learning-based tool for monitoring the quality of CMS RPC detector currents, enhancing stability and maintenance in the high-luminosity environment of the LHC.

Contribution

It introduces a novel automated monitoring system using machine learning, including generalized linear, autoencoder, and hybrid models for RPC current prediction.

Findings

Effective detection of anomalies in RPC currents.

Improved prediction accuracy with hybrid models.

Enhanced stability monitoring of CMS RPC detectors.

Abstract

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future.