The power of principled bayesian methods in the study of stellar evolution

¹ Department of Physical Sciences, Embry-Riddle Aeronautical University, 600 S. Clyde Morris Blvd, Daytona Beach, FL 32114, USA
² Statistics Section, Department of Mathematics, Imperial College London, SW7 2AZ, UK
³ Department of Statistics, University of California, Irvine, CA 92617, USA
⁴ Argiope Technical Solutions, LLC, 816 SW Watson St., Fort White, FL 32038, USA
⁵ Department of Physics and Astronomy, James Madison University, 901 Carrier Dr, MSC 4502, Harrisonburg, VA 22807, USA
⁶ Statistics Department, The Wharton School, University of Pennsylvania, 400 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104, USA
⁷ Department of Astronomy, University of Texas at Austin and Department of Mathematics and Statistics, University of Vermont, 16 Colchester Ave, Burlington, VT 05401, USA
⁸ Department of Physics & Astronomy, Dartmouth College, 6127 Wilder Laboratory, Hanover, NH 03755, USA

e-mail: ted.vonhippel@erau.edu

Abstract

It takes years of effort employing the best telescopes and instruments to obtain high-quality stellar photometry, astrometry, and spectroscopy. Stellar evolution models contain the experience of lifetimes of theoretical calculations and testing. Yet most astronomers fit these valuable models to these precious datasets by eye. We show that a principled Bayesian approach to fitting models to stellar data yields substantially more information over a range of stellar astrophysics. We highlight advances in determining the ages of star clusters, mass ratios of binary stars, limitations in the accuracy of stellar models, post-main-sequence mass loss, and the ages of individual white dwarfs. We also outline a number of unsolved problems that would benefit from principled Bayesian analyses.