The Interpretation of Color-Magnitude Diagrams through Numerical Simulation and Bayesian Inference

Details The Interpretation of Color-Magnitude Diagrams through Numerical Simulation and Bayesian Inference The Interpretation of Color-Magnitude Diagrams through Numerical Simulation and Bayesian Inference

May 1996

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996apj...462..672t&link_type=abstract

Astrophysical Journal v.462, p.672

Astronomy and Astrophysics

Astronomy

104

Stars: Hertzsprung-Russell Diagram, Methods: Statistical

Scientific paper

We present a new method designed to aid in the interpretation of the color-magnitude diagrams (CMDs) of resolved stars in nearby galaxies. A CMD is a two-dimensional distribution of data points with well understood Gaussian measurement errors created from two independent observations. The most rigorous way to interpret a CMD is to create a model CMD through Monte Carlo simulation using theoretical stellar evolution tracks to see what combination of initial conditions provides the best match with the observed data. In this paper we describe how best to quantitatively compare these types of model and data. A good model CMD must contain a spatial distribution of points that matches the data and also has the same relative numbers of red stars, blue stars, and any other features seen in the data. This kind of detailed information can be obtained by using the assumptions of Bayesian inference to calculate the likelihood of a model CMD being a good match to the data CMD. To illustrate the effectiveness of this approach, we have created several test scenarios using simplified data sets. We have derived a method that allows us to determine whether a model is distributed as the data over the entire CMD, and whether the relative numbers of points in parts of the diagram are different. We can also determine whether a good match can be made to part of the data, which will be useful when attempting to model complex star formation histories. Our examples show that the results are very sensitive to the size of the measurement errors in the data, and so it is only the accuracy of these errors that restricts our ability to distinguish the good from the bad models. Our method is sufficiently robust and automated that we can search through large areas of parameter space without having to inspect the models visually.

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