4 papers
Differentiable Forward Modeling for Efficient and Accurate Shear Inference
Ismael Mendoza, Axel Guinot, Matthew R. Becker +6
Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientif…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Blind Deconvolution in Astronomy: How Does a Standalone U-Net Perform?
Jean-Eric Campagne
Aims: This study investigates whether a U-Net architecture can perform standalone end-to-end blind deconvolution of astronomical images without any prior knowledge of the Point Spr…
Galaxy Imaging with Generative Models: Insights from a Two-Models Framework
Jean-Eric Campagne
Generative models have recently revolutionized image generation tasks across diverse domains, including galaxy image synthesis. This study investigates the statistical learning and…