Shoubaneh Hemmati (IPAC)

Reducing Extragalactic Dimensions // In this talk, I will present the results of multi-band imaging and spectroscopic observations to characterize the fundamental galaxy properties necessary in comprehending not only galaxy evolution but also cosmology. I will focus on the effectiveness of recently developed dimensionality reduction machine learning techniques in measuring the physical properties of galaxies and galaxy classifications. To achieve this, I will discuss, among other things, the LSST+WFIRST photometric redshift measurements, integrated and resolved measurement of physical properties of galaxies with deep HST observations, nebular and stellar dust extinction across the disk of emission line galaxies with high resolution Keck observations, luminosity function of local [CII] emitters, and the SPHEREX data-driven simulation. I finish with a discussions of a way forward, given the existing observations and models, for the next generation large area surveys.