2 citations · 5 across the 11 of their papers we have counts for
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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…
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…