AI RESEARCH
Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment
arXiv CS.LG
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ArXi:2604.06289v1 Announce Type: cross In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at CERN's Large Hadron Collider (LHC) by classifying a beam-loss monitor (BLM) time series during crystal rotation. We formalize a local robustness property for this classifier under an adversarial threat model based on real-world plausibility.