Time-Series Deep Learning Anomaly Detection for Particle Accelerators

Abstract

High energy particle accelerators rely on superconducting radio frequency cavities to transfer energy and accelerate the beam. Such particle accelerators are complex and expensive systems prone to failures which lead to downtime of the whole experimental facility: it is thus of primary importance to anticipate and prevent these faults to improve the uptime and cost-effectiveness of particle accelerators. Data-driven methods are especially fit for this task as they can leverage all the data recorded and archived by a typical control system. Previous works used classical machine learning (ML) models for anomaly detection to detect early signs of an upcoming fault. We propose here a different approach based on deep learning (DL) models, exploiting the temporal correlation of the raw data. Three different models are tested on data from the ALPI (Linear Accelerator for Ions) linear accelerator in INFN (National Institute for Nuclear Physics) Legnaro National Laboratories in Italy and they are compared with the classical ML approach.