Abstract
Abstract Ultracool dwarf stars and brown dwarfs provide a unique probe of large-scale Galactic structure and evolution; however, until recently spectroscopic samples of sufficient size, depth, and fidelity have been unavailable. Here, we present the identification of 164 M7-T9 ultracool dwarfs in 0.6 deg2 of deep, low-resolution, near-infrared spectroscopic data obtained with the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) instrument as part of the WFC3 Infrared Spectroscopic Parallel Survey and the 3D-HST survey. We describe the methodology by which we isolate ultracool dwarf candidates from over 200,000 spectra, and show that selection by machine-learning classification is superior to spectral index-based methods in terms of completeness and contamination. We use the spectra to accurately determine classifications and spectrophotometric distances, the latter reaching to ∼2 kpc for L dwarfs and ∼400 pc for T dwarfs.
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