Testing Substellar Models with Dynamical Mass Measurements

TL;DR

This study uses dynamical mass measurements of ultracool binaries to test and identify significant discrepancies in current substellar models, highlighting the need for more comprehensive data and refined methods.

Contribution

It provides the first dynamical mass measurements at low luminosities, revealing critical issues in existing models of ultracool dwarfs and brown dwarfs.

Findings

Color-magnitude diagrams do not reliably infer masses.

Effective temperatures from models are inconsistent with spectral fits.

Measured luminosities are higher than model predictions for known brown dwarf pairs.

Abstract

We have been using Keck laser guide star adaptive optics to monitor the orbits of ultracool binaries, providing dynamical masses at lower luminosities and temperatures than previously available and enabling strong tests of theoretical models. We have identified three specific problems with theory: (1) We find that model color-magnitude diagrams cannot be reliably used to infer masses as they do not accurately reproduce the colors of ultracool dwarfs of known mass. (2) Effective temperatures inferred from evolutionary model radii are typically inconsistent with temperatures derived from fitting atmospheric models to observed spectra by 100-300 K. (3) For the only known pair of field brown dwarfs with a precise mass (3%) and age determination (~25%), the measured luminosities are ~2-3x higher than predicted by model cooling rates (i.e., masses inferred from Lbol and age are 20-30% larger than measured). To make progress in understanding the observed discrepancies, more mass measurements spanning a wide range of luminosity, temperature, and age are needed, along with more accurate age determinations (e.g., via asteroseismology) for primary stars with brown dwarf binary companions. Also, resolved optical and infrared spectroscopy are needed to measure lithium depletion and to characterize the atmospheres of binary components in order to better assess model deficiencies.