Monte Carlo Stellar Evolution Models and Isochrones

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From determining the radius of exoplanet hosting stars, the ages of individual stars, to the age of distant protogalaxies, the use of stellar evolution models is ubiquitous in astrophysics. Stellar models require the use of a variety of physical parameters (such as nuclear reaction rates and opacities) and make a variety of assumptions (such as the nature of convection). The Principal Investigator and his students at Dartmouth College will use the best data available to test and improve stellar models. Their Monte Carlo approach will provide an estimate of the uncertainties associated with stellar modeling. The proposed research will form the core of student's PhD thesis. Through Dartmouth’s Women in Science Project, first year undergraduate women, jointly mentored by the PI and the graduate student, will be employed to assist in the project. The PI has developed an astronomy foreign studies program, which takes Dartmouth students to South Africa for an intensive 10-week introduction to astronomy and a rich introduction to South African culture, history, and scientific research.This project will test and improve stellar models by comparing the predicted colors and absolute magnitudes of the stellar models to relatively local stars which have accurate parallax distances obtained by the Gaia satellite in its most recent data release (DR3). It is important to have an understanding of the uncertainties associated with stellar models and isochrones. Instead of generating a single set of stellar models and isochrones which are based upon the best estimates for the various input parameters to stellar evolution models, a Monte Carlo approach will be used. For each physical stellar parameter (mass and chemical composition), a suite of a 1000 stellar evolution models will be generated by randomly sampling the uncertainties in the various input parameters (opacity, nuclear reaction rates, etc.) used to generate the models. This suite of stellar evolution models will be used to generate a suite of isochrones, and from these the user will be able to extract not just the best estimate for a given quantity (such as the radius, luminosity or age of a star), but also their associated 68% and 95% confidence limits. These uncertainty estimates can be incorporated into subsequent analysis by those who use the stellar models and isochrones to ensure that the uncertainty in derived quantities (such as age) reflect not just observational uncertainties, but also uncertainties associated with the theoretical models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.