AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys

TL;DR

AS-Bridge is a novel bidirectional generative framework that models the relationship between ground-based LSST and space-based Euclid astronomical surveys, enabling joint analysis and prediction of missing data across surveys.

Contribution

It introduces a diffusion-based probabilistic model that explicitly captures the conditional distribution between two different astronomical surveys, facilitating inter-survey data translation and analysis.

Findings

Enables probabilistic predictions of missing survey observations.

Facilitates detection of rare astronomical events across surveys.

Demonstrates the feasibility of inter-survey generative modeling.

Abstract

The upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences in observational modality, sky coverage, point-spread function, and scanning cadence make joint analysis beneficial, but also challenging. To facilitate joint analysis, we introduce A(stronomical)S(urvey)-Bridge, a bidirectional generative model that translates between ground- and space-based observations. AS-Bridge learns a diffusion model that employs a stochastic Brownian Bridge process between the LSST and Euclid observations. The two surveys have overlapping sky regions, where we can explicitly model the conditional probabilistic distribution between them. We show that this formulation enables new scientific capabilities beyond single-survey analysis, including faithful probabilistic predictions of missing survey observations and inter-survey detection of rare events. These results establish the feasibility of inter-survey generative modeling. AS-Bridge is therefore well-positioned to serve as a complementary component of future LSST-Euclid joint data pipelines, enhancing the scientific return once data from both surveys become available. Data and code are available at \href{https://github.com/ZHANG7DC/AS-Bridge}{https://github.com/ZHANG7DC/AS-Bridge}.