Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
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Computer Science > Machine Learning
Title:Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Abstract:The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-driven methods. We present a Wasserstein-GAN-inspired approach for unsupervised inference of physically interpretable transformation parameters that map a changed detector response distribution back to a nominal reference distribution. In contrast to standard generative modeling, the generator is used as a learnable calibration transformation whose trainable weights represent the sought parameters, while the critic provides a distributional distance signal via the Wasserstein objective. We validate the approach on a tracking-detector toy model with controlled layer shifts and demonstrate its application on high-granularity Geant4-simulated calorimeter data with cell-wise aging effects. The method recovers aging coefficients for individual cells with correlation to ground truth and improves agreement between calibrated and reference energy-sum distributions, while exhibiting the expected degradation at increasing channel-to-channel noise levels. These results indicate that adversarial distribution matching can serve as a data-driven component of calibration strategies in settings where direct labels for degradation parameters are unavailable.
| Comments: | This is a preprint sent to Nuclear Science and Techniques journal |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18561 [cs.LG] |
| (or arXiv:2606.18561v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18561
arXiv-issued DOI via DataCite (pending registration)
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