Statistical analysis of stellar evolution

TL;DR

This paper discusses a Bayesian statistical framework utilizing complex computer models to analyze stellar evolution, accounting for data contamination and unresolved binaries to compare physics-based models of star development.

Contribution

It introduces a Bayesian approach integrating computer models into likelihood functions for detailed stellar evolution analysis.

Findings

Effective correction for field star contamination

Incorporation of unresolved binary star effects

Comparison of competing stellar evolution models

Abstract

Color-Magnitude Diagrams (CMDs) are plots that compare the magnitudes (luminosities) of stars in different wavelengths of light (colors). High nonlinear correlations among the mass, color, and surface temperature of newly formed stars induce a long narrow curved point cloud in a CMD known as the main sequence. Aging stars form new CMD groups of red giants and white dwarfs. The physical processes that govern this evolution can be described with mathematical models and explored using complex computer models. These calculations are designed to predict the plotted magnitudes as a function of parameters of scientific interest, such as stellar age, mass, and metallicity. Here, we describe how we use the computer models as a component of a complex likelihood function in a Bayesian analysis that requires sophisticated computing, corrects for contamination of the data by field stars, accounts for complications caused by unresolved binary-star systems, and aims to compare competing physics-based computer models of stellar evolution.