Open AccessApplying Random Forest Classification to Ultracool Dwarf Discovery in Deep Surveys. I. Color Classification with SDSS, UKIDSS, and WISE Photometry
Author(s) -
Zijie Gong,
Adriava Vega,
Eduardo Gauna Gutierrez,
Arantxa Mendiola Maytorena,
Carlos Verdaguer,
Christian Aganze,
Christopher Danner,
Adam J. Burgasser
Publication year - 2022
Publication title -
research notes of the aas
Language(s) - English
Resource type - Journals
ISSN - 2515-5172
DOI - 10.3847/2515-5172/ac6521
Subject(s) - random forest , photometry (optics) , physics , stellar classification , astrophysics , pattern recognition (psychology) , artificial intelligence , computer science , stars
In this first of two studies, we apply a random forest model to classify ultracool dwarfs from broadband color information. Using the Skrzypek et al. ultracool dwarf sample and a set of background sources, we trained a random forest classifier based on 28 colors derived from optical and infrared photometry from SDSS, UKIDSS, and WISE. Our model achieves 99.7% accuracy in segregating L- and T-type UCDs from background sources, and 97% accuracy in separating spectral subgroups. A separate random forest regressor model achieved a spectral classification precision of 1.3 subtypes. We applied these models to a 12.6 deg 2 region with overlapping SDSS, UKIDSS, and WISE coverage and identified 35 UCD candidates, five of which are previously reported, of which four are photometrically or spectroscopically classified UCDs. Our random forest model can be applied to multiple surveys to greatly expand the known census of UCDs.