4 papers
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…
MADNESS Deblender: Maximum A posteriori with Deep NEural networks for Source Separation
Biswajit Biswas, Eric Aubourg, Alexandre Boucaud +4
Due to the unprecedented depth of the upcoming ground-based Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory, approximately two-thirds of the galaxies are li…
The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending
Ismael Mendoza, Andrii Torchylo, Thomas Sainrat +18
We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algori…
Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions
Alessio Spagnoletti, Alexandre Boucaud, Marc Huertas-Company +2
Deconvolution of astronomical images is a key aspect of recovering the intrinsic properties of celestial objects, especially when considering ground-based observations. This paper…