Learning What's Real: Disentangling Signals and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the underlying physics. Moreover, it poses significant challenges for combining data in heterogeneous or multi-instrument frameworks. To disentangle these factors of variation, we propose a dual-encoder architecture with a counterfactual generation objective that leverages overlapping observations. The resulting representations explicitly separate intrinsic signals from sensor-specific distortions and noise, and can be used for counterfactual view generation, parameter inference, and instrument-independent similarity search---all unconfounded by measurement artifacts. We demonstrate the effectiveness of our approach in a multi-instrument setting on astrophysical galaxy images from the DESI Legacy Imaging Survey (Legacy) and the Hyper Suprime-Cam (HSC) Survey. This framework provides a general recipe for scientific self-supervised pretraining: construct training pairs from overlapping observations of the same physical system, treat sensor- or modality-specific effects as augmentations, and learn invariant representations through counterfactual generation.