93 citations · 305 across the 23 of their papers we have counts for
6 papers · 1 filter
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
Zhiwei Xue, Yuhang Li, Yash Patel +1
Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolutio…
Scalable Bayesian Inference for Finding Strong Gravitational Lenses
Yash Patel, Jeffrey Regier
Finding strong gravitational lenses in astronomical images allows us to assess cosmological theories and understand the large-scale structure of the universe. Previous works on len…
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…
Variational Inference for Deblending Crowded Starfields
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier
In images collected by astronomical surveys, stars and galaxies often overlap visually. Deblending is the task of distinguishing and characterizing individual light sources in surv…
Celeste: Variational inference for a generative model of astronomical images
Jeffrey Regier, Andrew Miller, Jon McAuliffe +5
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference. Each pixel intensity is treated as a Poisson random vari…