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20202024
most citedStar-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

18 citations · 51 across the 17 of their papers we have counts for

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astro-ph.IM2024

Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes

Chinedu Eleh, Yunli Zhang, Rafael Bidese +4

Stellar blends, where two or more stars appear blended in an image, pose a significant visualization challenge in astronomy. Traditionally, distinguishing these blends from single…

astro-ph.IM2022★ 1 cited

Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)

Imène R. Goumiri, Alec M. Dunton, Amanda L. Muyskens +2

Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the e…

astro-ph.IM2021★ 12 cited

Gaussian Process Classification for Galaxy Blend Identification in LSST

James J. Buchanan, Michael D. Schneider, Robert E. Armstrong +3

A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in…

astro-ph.IM2021★ 18 cited

Star-Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Amanda L. Muyskens, Imène R. Goumiri, Benjamin W. Priest +4

We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in h…

astro-ph.IM2020★ 3 cited

Star-Galaxy Separation via Gaussian Processes with Model Reduction

Imène R. Goumiri, Amanda L. Muyskens, Michael D. Schneider +2

Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Bein…