Inferring Properties of Brown Dwarfs with Empirical Relations and Data-Driven Models
Absolute magnitudes of brown dwarfs are important in understanding and modelling their evolution and spectra. One method to determine absolute magnitudes is to use empirical relationships with spectral types calibrated by objects with parallax measurements. Our work presents updated polynomial relations between spectral type and absolute magnitude based on a volume-limited sample.
Machine learning has been used in astronomical research as a tool to process and analyze large amounts of data, as well as to extract novel information. The Cannon has been utilized to infer stellar parameters and abundances from spectroscopic data by employing "known stellar labels". Using The Cannon we have developed a novel method to determine physical properties of brown dwarfs using data-driven models with results that are competitive to other techniques.