PAN seminar by Dr. John Wu (Johns Hopkins University and Space Telescope Science Institute)

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Date: July 31, 2020
Time: 3:30 p.m. - 4:30 p.m.
Location: zoom | Map
815 Pallister St
Detroit, 48202
Category: Seminar

Title - Insights on galaxy evolution and morphology from deep learning

Abstract - The growth of galaxies is regulated by the amount of cold gas available to form stars. In order to constrain galaxy evolution models, it is critical to measure the interstellar gas mass and the abundance of heavy elements ("metallicity") in the gas phase for large samples of galaxies. However, these properties are observationally difficult to measure, and galaxies' cold gas reservoirs are mostly invisible at optical wavelengths. One way to circumvent these challenges is to rely on the morphologies of galaxies, which are linked to their star formation and evolutionary histories. Collaborators and I have developed deep learning methods for estimating the gas content and metallicity of galaxies from imaging data alone. I will provide an overview of convolutional neural networks and their use cases in the astronomical image domain. I will also discuss novel ways to probe galaxy evolution using neural networks and visualization algorithms. Interpretable and accurate deep learning tools will enable us to multiply the scientific returns of large astronomical surveys.

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Meeting ID: 944 0805 2160
Passcode: 124700
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Contact

Raghav Kunnawalkam Elayavalli
7325329232
raghavke@wayne.edu

Cost

Free

Audience

Academic staff, Current students, Faculty