Stellar Population Synthesis Modeling: A Critical Evaluation

Princeton University, Princeton NJ

Investigators

Abstract

Dr. James Gunn (Princeton University) will undertake a detailed examination of the ingredients and methodology of stellar population synthesis (SPS) models. This type of modeling provides the bridge between the physical properties of galaxies (such as chemical composition, stellar mass, and star formation history) and the observed spectral energy distributions. It also represents the fundamental link between extragalactic observations and models of galaxy assembly. Despite its importance in many areas of extragalactic astronomy, the uncertainties associated with SPS modeling have received relatively little attention. This situation is especially troubling in light of known and important deficiencies in current implementations of SPS. In this project, Dr. Gunn will develop a flexible SPS code that is capable of incorporating uncertainties in stellar evolution, including the evolution and properties of evolved stars of all types. He will also explore uncertainties in stellar spectral libraries, models of dust in galaxies, the distribution of stellar abundances and the history of chemical enrichment, and the relative numbers of stars of different mass. The goals of the study are to provide robust constraints on the physical properties of large samples of galaxies, to investigate the extent to which uncertainties in SPS make it difficult to compare galaxy formation models to observations, and to highlight which uncertainties in SPS are the most important to address with future observations. The new, versatile SPS code developed under this proposal will be publicly released so that it may be maximally useful to the community. In addition, the code will be open source, which is unique for SPS codes. The project will also create comprehensive catalogs of a variety of physical properties of galaxies from existing and future surveys of galaxy properties. These catalogs will be fully released to the public on a short time-scale.