Gaussian Process Classification for Galaxy Blend Identification in LSST

Journal Article · 14 January 2022 · The Astrophysical Journal

DOI: https://doi.org/10.3847/1538-4357/ac35ca · OSTI ID:1840083

Buchanan, James J. ; Schneider, Michael D. ; Armstrong, Robert E.; Muyskens, Amanda L. ; Priest, Benjamin W. ; Dana, Ryan J.

Abstract 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 a so-called “blend.” The current standard method of assessing blend likelihood in LSST images relies on counting up the number of intensity peaks in the smoothed image of a blend candidate, but the reliability of this procedure has not yet been comprehensively studied. Here we construct a realistic distribution of blended and unblended galaxies through high-fidelity simulations of LSST-like images, and from this we examine the blend classification accuracy of the standard peak-finding method. Furthermore, we develop a novel Gaussian process blend classifier model, and show that this classifier is competitive with both the peak finding method as well as with a convolutional neural network model. Finally, whereas the peak-finding method does not naturally assign probabilities to its classification estimates, the Gaussian process model does, and we show that the Gaussian process classification probabilities are generally reliable.

• Sponsoring Organization: USDOE
• OSTI ID: 1840083
• Journal Information: The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 2 Vol. 924; ISSN 0004-637X
• Publisher: American Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English