Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Researchers have developed a deep learning framework designed to separate true signals from measurement artifacts in multi-sensor data. This method uses a dual-encoder architecture and a counterfactual generation objective to disentangle intrinsic physical properties from sensor-specific distortions. The framework's effectiveness was demonstrated on astrophysical galaxy images from the DESI Legacy Imaging Survey and the Hyper Suprime-Cam Survey, offering a general approach for scientific and multi-modal self-supervised pretraining. AI
IMPACT Provides a generalizable method for improving data analysis in scientific and multi-modal settings by disentangling true signals from measurement artifacts.