A physics-informed self-supervised framework learns detector calibration parameters and ionic charge-state predictions jointly from raw spectrometer data using iterative pseudo-labelling driven by physical constraints.
IEEE Transactions on Nuclear Science 63, 878–897
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CNN variants with reordered temporal and channel operations plus per-pulse reweighting reach pooled AUC-PR 0.816 on public HVCM dataset, beating prior methods on most subsystems and five fault families.
citing papers explorer
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Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
A physics-informed self-supervised framework learns detector calibration parameters and ionic charge-state predictions jointly from raw spectrometer data using iterative pseudo-labelling driven by physical constraints.
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Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
CNN variants with reordered temporal and channel operations plus per-pulse reweighting reach pooled AUC-PR 0.816 on public HVCM dataset, beating prior methods on most subsystems and five fault families.