The Astronomical Journal (Jan 2024)

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

  • Hunter Brooks,
  • Dan Caselden,
  • J. Davy Kirkpatrick,
  • Yadukrishna Raghu,
  • Charles A. Elachi,
  • Jake Grigorian,
  • Asa Trek,
  • Andrew Washburn,
  • Hiro Higashimura,
  • Aaron M. Meisner,
  • Adam C. Schneider,
  • Jacqueline K. Faherty,
  • Federico Marocco,
  • Christopher R. Gelino,
  • Jonathan Gagné,
  • Thomas P. Bickle,
  • Shih-Yun Tang,
  • Austin Rothermich,
  • Adam J. Burgasser,
  • Marc J. Kuchner,
  • Paul Beaulieu,
  • John Bell,
  • Guillaume Colin,
  • Giovanni Colombo,
  • Alexandru Dereveanco,
  • Deiby Pozo Flores,
  • Konstantin Glebov,
  • Leopold Gramaize,
  • Les Hamlet,
  • Ken Hinckley,
  • Martin Kabatnik,
  • Frank Kiwy,
  • David W. Martin,
  • Raúl F. Palma Méndez,
  • Billy Pendrill,
  • Lizzeth Ruiz,
  • John Sanchez,
  • Arttu Sainio,
  • Jörg Schümann,
  • Manfred Schonau,
  • Christopher Tanner,
  • Nikolaj Stevnbak,
  • Andres Stenner,
  • Melina Thévenot,
  • Vinod Thakur,
  • Nikita V. Voloshin,
  • Zbigniew Wȩdracki,
  • The Backyard Worlds: Planet 9 Collaboration

DOI
https://doi.org/10.3847/1538-3881/ad77d2
Journal volume & issue
Vol. 168, no. 5
p. 211

Abstract

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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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