Paper

Discovery of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

Ruiz, Lizzeth, Kiwy, Frank, Elachi, Charles, Caselden, Dan, Meisner, Aaron, Bell, John, Higashimura, Hiro, Gelino, Christopher, SchÜmann, JÖrg, Thakur, Vinod, Schonau, Manfred, Grigorian, Jake, Bickle, Thomas, Yadukrishna Raghu, Burgasser, Adam, Flores, Deiby, Shih-yun, Tang, Wedracki, Zbigniew, Washburn, Andrew, Colombo, Giovanni, Pendrill, William, Voloshin, Nikita, Gagné, Jonathan, Rothermich, Austin, Gramaize, Leopold, Martin, David, Sainio, Arttu, Dereveanco, Alexandru, Glebov, Konstantin, Stenner, Andrés, Brooks, Hunter, Faherty, Jacqueline, Kuchner, Marc J, Beaulieu, Paul, Palma, Raul, Hinckley, Ken, Thévenot, Melina, Hamlet, Les, Sanchez, John, Tanner, Christopher, Trek, Asa, Kabatnik, Martin, J Davy Kirkpatrick, Marocco, Federico, Guillaume, Colin, Schneider, Adam, Nikolaj Stevnbak Andersen

2024

Overview

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named \\texttt{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 2 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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Infrared imagery / Infrared photometry / Machine learning / Photometry