Unsupervised Machine Learning for Fault detection in turn by turn Beam Position Monitors
Jakub Potaczala, Irfu/DACM et Université Paris-Saclay
Jeudi 16 juillet à 13h30, CEA-Saclay, Bat 130, pièce 102
Reporté
Turn-by-turn beam position monitors (TbT-BPMs) are essential for reconstructing the magnetic optics of high-luminosity colliders, yet faulty sensors and measurement noise routinely compromise data quality. As accelerators scale toward facilities like the Future Circular Collider (FCC), with thousands of BPMs distributed across a 91-km ring, manual inspection and hardware redundancy no longer suffice, motivating fully automated, unsupervised diagnostic pipelines. This seminar presents the improvements developed during an internship to a tool under development at CEA in collaboration with LISN at CentraleSupelec. The tool is used to study and compare complementary unsupervised machine learning approaches to TbT-BPM data from SuperKEKB's High and Low Energy Rings (HER/LER). A new pipeline including Isolation Forest, DBSCAN-based clustering coupled with two feature-engineering philosophies (a fully data-driven approach, Time2Feat, and a physics-informed fixed-feature approach) and a 1D convolutional autoencoder. Overall, these methods converge on a shared conclusion: unsupervised, physically interpretable pipelines can match or exceed manual fault curation, offering a scalable foundation for autonomous BPM diagnostics in next-generation accelerators.
Les IA Classification of VHF images through Machine Learning
Baptiste Hubert, Irfu/Dap; Stéphane Schanne, Irfu/Dap
Lundi 9 février à 12h, CEA-Saclay, Orme-des merisiers, salle kepler
The SVOM satellite mission, launched in June 2024 is dedicated to Gamma-Ray Burst (GRBs) studies. The ECLAIRs trigger onboard SVOM, which reorients the satellite for GRB follow-up observations, also provides a real-time Alert Sequence for each detected GRB, transmitted to ground over the SVOM VHF receiver network. One of the two trigger algorithms, the Image Trigger (IMT) transmits at the end of each Alert Sequence a Sub-Image centered on the detected source, which is currently used by the trigger team on ground to manually identify a true point-like source from a false alert, by observing the shape in the Sub-Image. We study multiple machine learning algorithms, such as pre-trained Convolutional Neural Networks (CNNs), to perform the automatic classification of the transmitted Sub-Image and rapid identification of False Alerts. This algorithm is intended to be first used on ground at the SVOM French Science Center, and in a later stage may be implemented on board in a future flight software update.