Estimating precise metallicity and stellar mass evolution of galaxies
Abstract
The evolution of galaxies can be conveniently broken down into the evolution of their contents. The changing dust, gas, and stellar content in addition to the changing dark matter potential and periodic feedback from a super-massive blackhole are some of the key ingredients. We focus on the stellar content that can be observed, as the stars reflect information about the galaxy when they were formed. We approximate the stellar content and star formation histories of unresolved galaxies using stellar population modeling. Though simplistic, this approach allows us to reconstruct the star formation histories of galaxies that can be used to test models of galaxy formation and evolution. These models, however, suffer from degeneracies at large lookback times (t > 1 Gyr) as red, low luminosity stars begin to dominate a galaxy’s spectrum. Additionally, degeneracies between stellar populations at different ages and metallicities often make stellar population modeling less precise. The machine learning technique diffusion k-means has been shown to increase the precision in stellar population modeling using a mono-metallicity basis set. However, as galaxies evolve, we expect the metallicity of stellar populations to vary. We use diffusion k-means to generate a multi-metallicity basis set to estimate the stellar mass and chemical evolution of unresolved galaxies. Two basis sets are formed from the Bruzual & Charlot 2003 and MILES stellar population models. We then compare the accuracy and precision of these models in recovering complete (stellar mass and metallicity) histories of mock data. Similarities in the groupings of stellar population spectra in the diffusion maps for each metallicity hint at fundamental age transitions common to both basis sets that can be used to identify stellar populations in a given age range.
- Publication:
-
American Astronomical Society Meeting Abstracts #231
- Pub Date:
- January 2018
- Bibcode:
- 2018AAS...23114909M