Project Description: Galaxies at the low mass end (often called dwarf galaxies) are a unique proving ground for galaxy formation and the nature of dark matter; often observations and theoretical expectations are in tension at this mass scale. In this project, the student will take a new sample of dwarf galaxies identified via a convolutional neural network algorithm and study their physical properties. In particular, they will use ultraviolet data from NASA's Neil Gehrels Swift satellite, which is sensitive to recent star formation. The student will request the data from the satellite through an approved program, analyze the incoming data, and measure the star formation properties of these new dwarf galaxies. They will then compare the properties of these dwarfs to those in different environments, and draw conclusions about how stars form in the smallest galaxies. If there is time, the student will get an opportunity to write a paper based on this study.
NASA Relevance: The data for this project will be directly from the Neil Gehrels Swift Observatory, which is a NASA satellite which specializes in ultraviolet observations.
Work Description: The student should be organized and responsible. They are expected to keep careful notes of what they do, and to be able to describe it. The student will request observations of the dwarf galaxies through a web portal. When the data is taken, the student will download the data and perform standard data analysis tasks that we will teach them. The student will make summary plots based on the data analysis, and in the end be able to write up their results in a coherent manner.
Open or Reserved Project: Open, 1 position