5 papers
Neural Posterior Estimation for Inferring Weak Lensing Shear
Tim White, Dingrui Tao, Camille Avestruz +2
The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to co…
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…
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…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
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…