用对抗学习修复传感器老化导致的数据分布偏移
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
- 用Wasserstein GAN思想设计可解释的校准变换模型
- 在模拟数据中准确恢复单个探测器单元的老化系数
- 适合无标签老化参数的传感器校准场景
传感器性能受运动和老化影响，导致采集数据质量下降，进而影响下游数据驱动方法。本文提出一种基于Wasserstein GAN的无监督方法，通过学习物理可解释的变换参数，将退化的探测器响应分布映射回基准分布。与传统生成建模不同，该方法将生成器作为可训练的校准变换，其权重即为待求参数；判别器则通过Wasserstein距离提供分布差异信号。在具有可控层偏移的追踪探测器模型上验证后，进一步应用于高精度Geant4模拟的量能器数据，考虑单元级老化效应。结果表明，该方法能有效恢复各单元老化系数（与真实值相关），改善校准后能量和分布与参考分布的一致性；在通道间噪声增加时性能按预期下降。说明对抗式分布匹配可作为无直接标签情况下的数据驱动校准组件。
原文摘要 · Abstract (English)
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.
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