Beyond the Local Volume. I. Surface Densities of Ultracool Dwarfs in Deep HST/WFC3 Parallel Fields

TL;DR

This study identifies 164 ultracool dwarfs using deep HST/WFC3 infrared spectroscopy, demonstrating machine learning's effectiveness in candidate selection and providing insights into their distribution and distances within the Galaxy.

Contribution

It introduces a new method combining machine learning with spectroscopic data to efficiently identify ultracool dwarfs and determine their classifications and distances.

Findings

164 ultracool dwarfs identified in deep HST/WFC3 data

Machine learning outperforms spectral index methods in candidate selection

Distances reach up to ~2 kpc for L dwarfs and ~400 pc for T dwarfs

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~deg$^2$ of deep, low-resolution, near-infrared spectroscopic data obtained with the Hubble Space Telescope Wide Field Camera 3 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 ~400pc for T dwarfs.