Using Old and New Approaches: Determining Physical Properties of Brown Dwarfs with Empirical Relations and Machine Learning Models

S. Jean Feeser; William M. J. Best

doi:10.1093/mnras/stac855

paper

Using Old and New Approaches: Determining Physical Properties of Brown Dwarfs with Empirical Relations and Machine Learning Models

arXiv:2203.13829 · doi:10.1093/mnras/stac855

Abstract

We investigate applications of machine learning models to directly infer physical properties of brown dwarfs from their photometry and spectra using $The Cannon$ . We demonstrate that absolute magnitudes, spectral types, and spectral indices can be determined from low-resolution SpeX prism spectra of L and T dwarfs without trigonometric parallax measurements and with precisions competitive with commonly used methods. For T dwarfs with sufficiently precise spectra and photometry, bolometric luminosities and effective temperatures can be determined at precisions comparable to methods that use polynomial relations as a function of absolute magnitudes. We also provide new and updated polynomial relations for absolute magnitudes as a function of spectral types L0-T8 in 14 bands spanning Pan-STARRS $r_{P 1}$ to AllWISE $W3$ , using a volume-limited sample of 256 brown dwarfs defined entirely by parallaxes. These include the first relations for brown dwarfs using Pan-STARRS1 photometry and the first for several infrared bands using a volume-limited sample. We find that our novel method with $The Cannon$ can infer absolute magnitudes with equal or smaller uncertainties than the polynomial relations that depend on trigonometric parallax measurements.

22 pages, 19 figures, 4 tables. Accepted for publication in MNRAS

Using Old and New Approaches: Determining Physical Properties of Brown Dwarfs with Empirical Relations and Machine Learning Models

Abstract

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