GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
Year: 2021
Type: article
Abstract: We present GHOST, a database of 16,175 spectroscopically classified supernovae (SNe) and the properties of their host galaxies. We have constructed GHOST using a novel host galaxy association method that employs deep postage stamps of the field surrounding a transient. Our gradient ascent method achieves fewer misassociations for low-z hosts and higher completeness for high-z hosts than previous methods. Using dimensionality reduction, we identify the host galaxy properties that distinguish SN classes. Our results suggest that the host galaxies of superluminous SNe, Type Ia SNe, and core-collapse SNe can be separated by brightness and derived extendedness measures. Next, we train a random forest model to predict SN class using only host galaxy information and the radial offset of the SN. We can distinguish Type Ia SNe and core-collapse SNe with ∼70% accuracy without any photometric or spectroscopic data from the event itself. Vera C. Rubin Observatory will usher in a new era of transient population studies, demanding improved photometric tools for rapid identification and classification of transient events. By identifying the host features with high discriminatory power, we will maintain SN sample purities and continue to identify scientifically relevant events as data volumes increase. The GHOST database and our corresponding software for associating transients with host galaxies are both publicly available through the astro_ghost package.
Source: The Astrophysical Journal
Authors: Alex Gagliano, Gautham Narayan, Andrew Engel, Matias Carrasco Kind
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, Smithsonian Institution, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Gordon and Betty Moore Foundation, Yale University, Carnegie Institution of Washington, Vanderbilt University, Ohio State University, California Institute of Technology, University of Washington, Johns Hopkins University, Carnegie Mellon University, University of Notre Dame, University of Portsmouth, New Mexico State University, National Centre for Supercomputing Applications, Space Telescope Science Institute, York University, University of Oxford, Queen's University Belfast, Durham University, Queen's University, Ministério da Ciência, Tecnologia e Inovação, Centre National de la Recherche Scientifique, National Central University, Universidad Nacional Autónoma de México, Eötvös Loránd Tudományegyetem, Max-Planck-Institut für Astrophysik, Max-Planck-Institut für Astronomie, Instituto de Astrofísica de Canarias, University of Illinois at Urbana-Champaign, Office of Science, Jet Propulsion Laboratory, University of Colorado Boulder, Smithsonian Astrophysical Observatory, Science Mission Directorate, Science and Technology Facilities Council, Leibniz-Gemeinschaft, Institut National de Physique Nucléaire et de Physique des Particules, Planetary Science Division, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory
Awards: G1079818582, G1141912172, G1143651532, G1229526393, G1286236842, G1632040200, G1863003419, G2371548206, G303061748, G3083819904, G3884951186, G419881185, G42916606, G4474636675, G4501827968, G4654690619, G495301720, G498139845, G5004724115, G5438923238, G5473946529, G5570207115, G618131832, Graduate Research Fellowship Program (GRFP), G6447494072, G6652958823, A NEO SEARCH PROGRAM USING THE PAN-STARRS PS1 TELESCOPETHE GOAL OF THE PROPOSED RESEARCH IS TO USE T, G7146827954, G7187964967, G7354628648, Pan-STARRS1: Operations; Public Data Release; Education and Outreach, G7950275392, G833430883, G8382000481, G8437677540, G8726098666, G969889393
OpenAlex ID: W3081097224