2 citations · 5 across the 11 of their papers we have counts for
12 papers
Probabilistic characterization of blending with LSST and application to cluster lensing cosmology
Manon Ramel, Cyrille Doux, Marine Kuna +9
Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise mea…
Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
Tim White, Shreyas Chandrashekaran, Camille Avestruz +2
Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear…
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…
Constraining Galaxy Cluster Triaxiality via Weak Lensing --- I. Preparation for the Rubin Data Beyond Leading Order
Shenming Fu, Radhakrishnan Srinivasan, Tae-hyeon Shin +13
The 3D mass distributions of galaxy clusters are generally triaxial, a geometry that is difficult to constrain from projected observations. In this work, we measure the projected h…
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…
Simulation-Based Inference for Probabilistic Galaxy Detection and Deblending
Ismael Mendoza, Derek Hansen, Runjing Liu +6
Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As…
Neural Posterior Estimation for Cataloging Astronomical Images from the Legacy Survey of Space and Time
Yicun Duan, Xinyue Li, Camille Avestruz +2
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will commence full-scale operations in 2026, yielding an unprecedented volume of astronomical images. Construct…
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
Aakash Patel, Tianqing Zhang, Camille Avestruz +2
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astr…
Weak lensing mass-richness relation of redMaPPer clusters in the LSST DESC DC2 simulations
Constantin Payerne, Zhuowen Zhang, Michel Aguena +13
Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, where clusters are detected through their richness, clu…
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…
Statistical Inference for Coadded Astronomical Images
Mallory Wang, Ismael Mendoza, Cheng Wang +2
Coadded astronomical images are created by stacking multiple single-exposure images. Because coadded images are smaller in terms of data size than the single-exposure images they s…
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
Derek Hansen, Ismael Mendoza, Runjing Liu +4
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based on deep gene…