1% accuracy in fundamental stellar parameters? Not without an extensive redesign of eclipsing binary models.

Villanova University, Villanova PA

Investigators

Abstract

Eclipsing binary stars can be used to determine the radius and mass of stars by relying on simple geometry and well understood laws of physics. Observations obtained over the last decade have greatly improved in accuracy, so the theoretical models used to interpret these data require substantial revision. This project identifies the aspects of theoretical models in need of revision and proposes a path to update the models. This will enable accurate (within 1%) measurements of stellar masses and radii. Precise determination of fundamental stellar properties is critical to many branches of astrophysics, from studying the life cycles of stars to determining distances throughout the universe. The advances in modelling approaches and classification are applicable to many problems in astronomy, physics, engineering, and applied mathematics. Conducting this program at a primarily undergraduate institution will provide opportunities undergraduate students learn modern research tools and techniques. The investigators will develop the following improvements to their eclipsing binary modelling codes: (a) optimize stellar surface discretization to mitigate systematics in distorted systems and to be able to model more complicated structures such as accretion disks; (b) systematically treat Doppler boosting for all modelled binaries; (c) devise a numerical framework for the Generalized Roche Potential that will allow for tilted spin axes of components to the orbital axis; (d) rigorously treat multiple body systems, predicting eclipse timing variations, tertiary eclipses, dynamical interactions of extended bodies, and other related phenomena; (e) revise the limb darkening treatment to resolve the ingress/egress fitting problems and, consequently, eliminate systematics from the determination of stellar radii; (f) implement a new morphological classification scheme based on modern embedding algorithms; (g) provide a new formalism for statistical error estimates using Markov Chain Monte Carlo methods and Bayesian inference to resolve a long-standing issue of underestimated parameter errors; (h) account for finite integration time corrections; and (i) write a back-end in python that will allow code parallelization in high performance computing and make it available to the broader astronomical community.