12:00–1:00 pm Zoom Room
Gregory Mosby, NASA, "Simplifying the analysis of galaxy star formation histories and near-infrared detectors"
The evolution of galaxies can be broken down into the evolution of their contents. 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 star formation histories of unresolved galaxies using stellar population modeling. We can use stellar population modeling of galaxies to test galaxy evolution and formation models. However, in the limit of low galaxy surface brightness, integrated spectra often have such low S/N that it hinders analysis with standard stellar population modeling techniques. To address this problem, we have developed a method that can recover galaxy star formation histories (SFHs) from rest frame optical spectra with S/N ~ 5 Å^-1 with a specific application to quasar host galaxies. We use the machine learning technique diffusion k-means to tailor the stellar population basis set, simplifying the analysis, and it is successful in recovering a range of galaxy SFHs. Our method has the advantage in recovering information from quasar host galaxies and could also be applied to the analysis of other low S/N galaxy spectra such as that typically obtained for high redshift objects and integral field spectroscopic surveys. I have now begun using diffusion k-means to generate a multi-metallicity basis set to estimate the stellar mass and chemical evolution of unresolved galaxies. In addition, I have begun work to fully characterizing today's HgCdTe photodiode arrays to lay the foundation for future near infrared detector development. Low read noise and well-characterized detectors are crucial in the emerging search for biosignatures in exoplanet atmospheres. Strides in analyzing NIR detector data can also be made using principles from machine learning.