ML-based tool for RPC currents quality monitoring

The CMS experiment has 1056 Resistive Plate Chambers (RPCs) in its muon system. Monitoring their currents is the first essential step towards the stability of the CMS RPC detector performance. An automated tool to carry out this task has been developed. It utilizes the ability of Machine Learning (ML) methods in the modelling of the behavior of the current of these chambers. Two types of ML approaches are implemented: Generalized Linear Models (GLM) and Autoencoders. Their performances are shown in this presentation. All the developed tools are integrated in a framework that can be easily accessed and controlled by a specially developed Web User Interface that allows the end user to work with the monitoring tool in a simple manner.