Gaussian Process Classification for Galaxy Blend Identification in LSST
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.
Cite
@article{arxiv.2107.09246,
title = {Gaussian Process Classification for Galaxy Blend Identification in LSST},
author = {James J. Buchanan and Michael D. Schneider and Robert E. Armstrong and Amanda L. Muyskens and Benjamin W. Priest and Ryan J. Dana},
journal= {arXiv preprint arXiv:2107.09246},
year = {2022}
}
Comments
20 pages, 6 figures, version accepted by ApJ