Speaker
Description
Deep learning-based anomaly detection in cargo containers using muon tomography is limited by the scarcity of anomalous data samples and the domain gap between simulation and real detector data. This research proposes a methodology to transfer anomaly detection from simulation to operational cargo container inspection. To this end, we exploit neural density estimators to approximate the distribution of benign cargo and sample physically plausible scans at scale. This generative capability drives a data augmentation strategy that combines simulated scenarios with real examples from dedicated measurement campaigns, enriching the training data with configurations that neither source alone can provide. Models pre-trained on the augmented samples are then adapted using real muon data acquisitions, allowing the learned description of normal cargo to incorporate detector responses, calibration effects, and structural variability that are absent from simulation. Anomalies emerge as statistically significant departures from this data-corrected baseline, with no prior specification of anomalous materials. We demonstrate an end-to-end deployment, from simulated scene generation to real container scans, and highlight the simulation-to-data mismatch, decisive for reliable anomaly detection.