Orateur
Description
Turn-by-turn beam position monitors (TbT-BPMs) are the primary
diagnostic for optics measurement and correction in high-luminosity colliders.
Reliable identification of faulty sensors is essential to any optics
reconstruction pipeline, yet manual inspection does not scale to the hundreds
of monitors in current rings or the thousands expected in future machines such
as the FCC. We apply two complementary unsupervised machine-learning methods
to TbT-BPM data from both rings of SuperKEKB: Density-Based Spatial Clustering
of Applications with Noise (DBSCAN) with automatic feature selection via the
Time2Feat library, and a one-dimensional convolutional autoencoder (1D-CNN AE)
that operates directly on raw data without labelled training
examples. Both methods are evaluated on 2024 data against a
reference list of hardware-damaged BPMs provided by the SuperKEKB operations
team. On the High Energy Ring (HER), DBSCAN detects faulty BPMs with a recall (TPR) of 83\%, while the 1D-CNN AE achieves 80\%. On the Low Energy Ring (LER), the AE recovers all known-faulty BPMs (100\% TPR) while
DBSCAN recovers three (50\%). The models further identify strong candidates for unreported hardware faults, ranking them among
the top outliers for the HER and LER measurement sessions.