Type: Talk
Session: Stellar Science and Crowded Fields
Author: Easton Honaker

Abstract: Brown dwarfs form the key, yet poorly understood, link between stellar and planetary astrophysics. These objects offer unique tests of Galactic structure and evolution, both physically and chemically, but observational limitations have inhibited the large-scale analysis of these objects to date. Major upcoming digital sky surveys will reveal unprecedented numbers of brown dwarfs, among even greater numbers of stellar objects, enabling the statistical study of brown dwarfs. To effectively parse these massive datasets and extract the comparatively rare brown dwarfs, we must understand how brown dwarfs will look in upcoming databases. To explore the photometric properties and spread of brown dwarfs, we construct a synthetic population of brown dwarfs in the Solar Neighborhood using Gaia-derived star formation rate histories and Galactic thin disk scale heights alongside observational mass, metallicity, and age relationships. We apply the Sonora Bobcat evolutionary models to assign parameters such as effective temperature, luminosity, and radius to the sample. We present luminosity, temperature, age, and metallicity distributions as functions of height above the Galactic thin disk. Then, we create low-resolution spectra for all objects using K-Nearest Neighbor regression to interpolate the Sonora Bobcat model spectra grid. We perform synthetic photometry using bandpasses for both upcoming (LSST, Euclid) and previous (Pan-STARRS, 2MASS) digital sky surveys. From the synthetic photometry, we identify the subset of observable objects for each observatory. We also explore the population's spread in photometric color space for single- and multi-observatory color indices. This work lays the foundation for a machine learning classification pipeline to effectively select brown dwarfs in major upcoming surveys.