Note: this project is no longer available! Please see my other project: Representing galaxies and their surroundings with graph neural networks

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PI:  John F. Wu (email, website)
Group Website: ISM*@ST
Project Duration: 12 month rotation project for a graduate student

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; can evolve into thesis project; RA funding is available

Project Abstract

The goal is to design and optimize a neural network capable of generating realistic galaxy images conditioned on their properties such as stellar masses, star formation rates, metallicities, ionization parameters, etc. The project will build on traditional techniques used for inferring galaxy properties, such as spectral energy distribution fitting, and novel deep learning approaches, such as conditional de-noising diffusion models. This work will serve serves as the basis for a Hierarchical Bayesian model that will jointly infer galaxy properties using imaging data and photometric catalogs, which will be critical for breaking stellar population model degeneracies (e.g. between dust, age, and metallicity) and enable us to better understanding how galaxies grow.

Background and Context

As galaxies grow and evolve, they leave signatures of their evolution on their spectra as well as their appearances. Galaxy spectra are often observed in several broadband filters, and the total amount of light across the galaxies at each wavelength is measured as a flux in each band. Astronomers have devoted great care to modeling synthetic stellar populations in order to match the observed fluxes across the electromagnetic spectrum; this technique is called spectral energy distribution (SED) fitting (e.g. Conroy 2013). However, the appearances of galaxies are often reduced to just a few morphological parameters (such as concentration, asymmetry, smoothness, Gini coefficients) or human-made classifications, and none of these capture the richness of morphological detail that we readily see in galaxy images.

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Wu's research group and collaborators have already exploited survey imaging data have attempted to pick up the slack on better characterizing galaxies' morphologies, and have done so using large imaging surveys in order to study a variety of topics related to galaxy evolution. These include estimating galaxy gas-phase metallicities (Wu & Boada 2019), predicting neutral hydrogen gas content (Wu 2020), separating AGN and star-forming systems (Holwerda, Wu, et al. 2021; Guo, Wu, & Sharon 2022), identifying rare nearby dwarf satellite galaxies (Wu et al. 2022; Darragh-Ford, Wu et al. in prep), and even predicting the entire optical spectrum of galaxies (Wu & Peek 2020, see above). In each case, deep convolutional neural networks play a key role in representing the morphological information found in galaxy images.

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