Description:

Abstract: Understanding the physical properties of galaxies and how they change through cosmic time allows us to learn about the cosmic expansion, gravity, and the physical mechanisms that regulate the growth of structures. My work focuses on developing better tools to extract information about galaxy properties, such as stellar mass, star formation history, dust attenuation, chemical enrichment history, and redshift, using data from large multi-wavelength galaxy surveys. In the last decade, the astronomy community has made immense progress in modeling and fitting the spectral energy distribution (SED) of galaxies. I will summarize some of the lessons we have learned in these years, and describe two ongoing projects. In the first, we use machine learning techniques trained on cosmological simulations to infer the properties of galaxies, and try to figure out how to “trust” the models once they are applied to data. In the second, we use Bayesian model selection to constrain star formation histories from SED fitting, in a quest to make results less model-dependent.

Speaker: Viviana Acquaviva, CUNY CityTech 

Website: https://www.drvivianaacquaviva.com

Host: Tjitske Starkenburg 

If you know someone who would be interested in attending this talk, please contact Yas Shemirani (yassaman.shemirani@northwestern.edu) to access the Zoom link.

Keywords: Physics, Astronomy, Astrophysics