AstroAI Lunch Talks - March 16, 2026 - Pablo Mercader Perez
16 Mar 2026 - Joshua Wing
The video can be found here: https://www.youtube.com/watch?v=zlBpfaPv6g4
Speaker: Pablo Mercader Perez (MIT)
Title: Disentangling Signal and Measurement Artifacts Using Multi-Sensor Data in Astrophysics
Abstract: 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 physics underlying the phenomena we observe. Furthermore, it limits our ability to combine observations in heterogeneous or multi-instrument settings. We propose a deep learning framework that leverages overlapping observations, a dual-encoder architecture, and a counterfactual generation objective to disentangle these factors of variation. 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. We demonstrate the effectiveness of our approach on astrophysical galaxy images from the DESI Legacy Imaging Survey (Legacy) and the Hyper Suprime-Cam (HSC) Survey as a representative multi-instrument setting.