Discovery of 118 New Ultracool Dwarf Candidates Using Machine-learning Techniques

Abstract We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine-learning tool, named SMDET , applied to time-series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate’s spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T-subdwarf candidate, two extreme T-subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for two objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine-learning tools as a new large-scale discovery technique.

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Publication Details

Journal
The Astronomical Journal
Published
2024-10-17
DOI
https://doi.org/10.3847/1538-3881/ad77d2
Citations
2
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.66

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article

Discovery of 118 New Ultracool Dwarf Candidates Using Machine-learning Techniques

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2 citations
The Astronomical Journal
Stellar, planetary, and galactic studies
0.66
article

Discovery of 118 New Ultracool Dwarf Candidates Using Machine-learning Techniques

Adam C. Schneider, G. Colombo, Jacqueline K. Faherty, Andrew Washburn, Shih-Yun Tang, Christopher R. Gelino, Paul Beaulieu, Nikita V. Voloshin, Martin Kabatnik, Dan Caselden, Adam J. Burgasser, Lizzeth Ruiz, Ken Hinckley, Frank Kiwy, J. Davy Kirkpatrick, Thomas P. Bickle, Konstantin Glebov, Federico Marocco, Vinod Thakur, Andrés Stenner, Jonathan Gagné, Charles A. Elachi, Manfred Schonau, Leslie K. Hamlet, Zbigniew Wędracki, Arttu Sainio, Hunter Brooks, Austin Rothermich, John B. Bell, Nikolaj Stevnbak Andersen, Aaron Meisner, Melina Thévenot, Léopold Gramaize, R. A. Méndez, Yadukrishna Raghu, J. Schümann, Guillaume Colin, Marc J. Kuchner, Alexandru Dereveanco, Billy Pendrill, Hiro 村滉 Higashimura 東, Jake Grigorian, David W. Martin, Christopher Tanner, Deiby Pozo Flores, John Sánchez, Asa Trek
article en
2 citations

Abstract

Abstract We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine-learning tool, named SMDET , applied to time-series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate’s spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T-subdwarf candidate, two extreme T-subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for two objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine-learning tools as a new large-scale discovery technique.

The Astronomical JournalVol. 168(5)
Pepperdine University (US), University of Southern California (US), The Graduate Center, CUNY (US), California Institute of Technology (US), Goddard Space Flight Center (US), American Museum of Natural History (US), City University of New York (US), Northern Arizona University (US), The Open University (GB), Lowell Observatory (US), Espace pour la vie (CA), University of California San Diego (US), Hunter College (US), NSF NOIRLab (US), United States Naval Observatory (US), Infrared Processing and Analysis Center (US), Université de Montréal (CA), Rice University (US)
National Science Foundation, Smithsonian Institution, National Aeronautics and Space Administration, Gordon and Betty Moore Foundation, Yale University, California Institute of Technology, Johns Hopkins University, University of Arizona, Space Telescope Science Institute, European Space Agency, Queen's University Belfast, Durham University, Queen's University, National Central University, Eötvös Loránd Tudományegyetem, Max-Planck-Institut für Astronomie, Jet Propulsion Laboratory, Smithsonian Astrophysical Observatory, Science Mission Directorate, Science and Technology Facilities Council, Planetary Science Division, Los Alamos National Laboratory
Openalex Percentile: Top 27%
Stellar, planetary, and galactic studies
0.66
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