0 Assigned ID: 1927 ----- Attempting Submission 1 (Fri Apr 09 14:19:53 GMT 2021) ----- HST Phase I Proposal 1927 successfully submitted. Receipt: # 1927-1 ----- Attempting Submission 2 (Fri Apr 09 16:23:08 GMT 2021) ----- Recovering physical properties from galaxies via spectral energy distribution (SED) modeling is dependent on a number of assumptions, including galaxy star foramtion histories and dust attenuation curves. As we show in this proposal, uncertainties in these assumptions canpropagate to significant errors in the derived stellar masses and star formation rates of galaxies. The fundamental goal of this proposal is to minimize the uncertainties incurred by these assumptions. To accomplish this, we propose to develop a new public-facing machine-learning assisted software that maps observational photometry to the derived physical properties of galaxies. Our proposed software utilizes synthetic SEDs from cosmological hydrodynamic galaxy formation simulations as a training set, and as we demonstrate, shows significant promise in outperforming modern state-of-the-art traditional SED fitting software. We will develop our new machine learning-assisted SED fitting software to (a) enable more accurate physical property determinations from both archival and future HST observations of galaxies near and far, and (b) couple with the ETC to aid observers in planning future observations that are motivated by determining particular physical properties to below a certain error threshold. Desika Narayanan (PI; University of Florida, Assistant Professor) is the PI of the powderday dust radiative transfer software package, and has significant experience in analyzing cosmological hydrodynamic galaxy formation simulations. Sidey Lower (Co-I; University of Florida, Graduate Student) is an expert in radiative transfer simulations in galaxies, and in SED fitting software. Her most recent paper analyzes the efficacy of non-parametric star formation histories in recovering the physical properties of galaxies in traditional SED fitting codes. Sankalp Gilda (Co-I; University of Florida, Graduate Student) is an expert in machine learning techniques, and has developed the stacked learning algorithms that we have employed for this proposal. Galaxies 0 Regular false false false true false SUPPORTED