Alert signal classification and prediction using natural language processing for the title calorimeter of atlas

Abstract

This study investigated temporal patterns and spatial inhomogeneities in the A Toroidal Large Hadron Collider ApparatuS Tile Calorimeter Detector Control System (ATLAS TileCal DCS) alarm logs to minimize downtime and enhance data collection efficiency for double Higgs boson production during high-luminosity (HL) operations of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) post the Phase-II upgrade in 2029. By applying Natural Language Processing (NLP) to detector control signals and machine learning (ML) techniques to train Long Short Term Memory (LSTM) models for both classification of future alarm types and forecasting categorical alarm rates in problematic modules, the approach achieved Mean Absolute Percentage Error (MAPE) scores between 12–23% and an overall classification accuracy of 75%. These results demonstrate the feasibility of a ML–driven predictive maintenance strategy, providing a possible early detection mechanism to optimize di-Higgs boson detection. Although further testing is needed before full integration, this LSTM-based framework shows promise for reducing unplanned downtime and accelerating scientific progress in the HL-LHC era.