Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment
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. Building on established parameterized input-transformation patterns used for transformation- and semantic-perturbation robustness, we instantiate a preprocessing-aware wrapper for our deployed time-series pipeline: we encode time-series normalization, padding constraints, and structured perturbations as ...
| Authors | Xaver Fink, Borja Fernandez Adiego, Daniele Mirarchi et al. |
|---|---|
| Primary category | cs.CR |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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