Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
This work addresses the need for automated calibration in high-energy physics and sensor systems where degradation parameters are unknown, offering a data-driven alternative to manual calibration.
The paper introduces a Wasserstein-GAN-based method for unsupervised calibration of sensor-induced distribution drift, recovering aging coefficients with high correlation to ground truth and improving energy-sum distribution agreement on simulated calorimeter data.
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.