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

Dichang Zhang, Yixuan Shao, Simon Birrer, Dimitris Samaras

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}.

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

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}.
Paper Structure (28 sections, 1 theorem, 16 equations, 4 figures, 2 tables)

This paper contains 28 sections, 1 theorem, 16 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Next-Generation Astronomical Surveys
  4. Strong Gravitational Lensing
  5. Image Translation
  6. Anomaly Detection
  7. Method
  8. Survey Translation Formulation
  9. AS-Bridge Framework
  10. Brownian Bridge
  11. Maximum Likelihood Training
  12. Rare event detection
  13. Experiments
  14. Dataset Generation
  15. Cosmology Configuration
  16. ...and 13 more sections

Key Result

Proposition 1

Training with $\epsilon$-prediction loss is equivalent to training with the loss define in Eq. (12) with a milder weighting term $\sqrt{\delta_t}$.

Figures (4)

  • Figure 1: Overview of AS-Bridge. The central panel shows the visible sky, with the LSST and Euclid survey footprints marked in blue and red, respectively. The overlapping regions indicate areas jointly observed by the ground-based LSST (left) and the space-based Euclid mission (right). LSST provides multi-band optical images that are more blended due to atmospheric seeing, while Euclid delivers sharper near-infrared observations from space. From these overlapping regions, matched image cutouts are extracted and used to train AS-Bridge, which models the probabilistic translation between the two survey domains using a Brownian bridge formulation.
  • Figure 2: LSST-to-Euclid translation. Left: LSST where nearby galaxies are strongly blended due to atmospheric seeing and PSF broadening. Middle: Euclid observation revealing the true multi-object structure at higher spatial resolution. Right: AS-Bridge reconstruction from the LSST input, correctly recovering the number of galaxies and accurately localizing their centers.
  • Figure 3: Euclid-to-LSST translation with multiple realizations. From left to right: LSST observation, Euclid observation, and four stochastic LSST reconstructions generated by AS-Bridge from the Euclid input. While Euclid provides high-resolution morphology but limited spectral information, AS-Bridge reconstructs plausible LSST multi-band appearances that preserve the underlying structure and produce color variations consistent with the ground truth.
  • Figure 4: Midpoint-to-Euclid AS-Bridge reconstructions for (a) a system containing a common in-distribution edge-on disk galaxy and (b) a rare galaxy–galaxy strong-lensing system. From left to right: LSST , Euclid , and AS-Bridge reconstruction of Euclid from midpoint. Although the two cases appear visually similar, AS-Bridge faithfully reconstructs the elongated disk morphology in (a), while in (b) it fails to reproduce the extended arc. This facilitates rare event detection.

Theorems & Definitions (1)

  • Proposition 1