Description:

Abstract: Galaxies process the raw materials of the universe. Thus, studying the stars in galaxies and their histories gives us insight into how the universe has changed with time, how galaxies likely formed, and how they will evolve. We commonly estimate the star formation histories of galaxies in a procedure called stellar population modeling. In this procedure, we traditionally compare the light from the sums of empirical populations of stars with identical age and chemical composition to the light of an observed galaxy. In our work, we use a machine learning algorithm to form a new basis set of average stellar populations to compare to the light of an observed galaxy. Even with noisy galaxy spectra, we showed we could derive accurate star formation histories of galaxies more precisely than a traditional method. I have now begun using similar techniques to generate a basis set to estimate the stellar mass and chemical evolution of unresolved galaxies. In addition, I continue my work to fully characterize today’s HgCdTe photodiode arrays and 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.

Speaker: Greg Mosby, NASA

Website: https://astrogregmosby.com/

Host: Kerry Paterson & Mel Ulmer

If you know someone who would be interested in attending this talk, please contact Samantha Westlake (samantha.westlake@northwestern.edu) to access the Zoom link.

Keywords: Physics, Astronomy, Astrophysics