2021/07/31 by James J. Buchanan, J. Buchanan, M. Schneider +8
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Astrophysics #Classifier (UML) #Computer science #Convolutional neural network #Fidelity #Galaxies: Formation, Evolution, Phenomena #Galaxy #Gamma-ray bursts and supernovae #Gaussian #Gaussian Processes and Bayesian Inference #Gaussian process #Mathematics #Mixture model #Parameter space #Pattern recognition (psychology) #Physics #Statistics #astro-ph.IM
paper · pdf · doi:10.3847/1538-4357/ac35ca
20 pages, 6 figures, version accepted by ApJ
arxiv created 2021/12/11 · openalex publication_date 2022/01/01 · arxiv updated 2022/01/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.