Recent Undergraduate Research Projects
Summer 2020 Undergraduate Research Projects
Gaussian Process Regression for Integrating the Transition Structure Factor Curve
Research mentors: Prof. James Shepherd (Chemistry), Tina Mihm (Chemistry)
The transition structure factor is a component of electron correlation energy which encodes useful information about the convergence behavior of a system. We explore the use of Gaussian Process Regression for completing the transition structure factor curve to attain the thermodynamic limit correlation energy at the cost of relatively small system sizes.
A Search for Dark Matter with HaloSat
Research Mentors: Prof. Philip Kaaret, Keith Jahoda (NASA/GSFC code 662), Lorella Angelini (NASA/GSFC code 661)An X-ray emission line near 3.5 keV observed in dark matter dominated objects (galaxy clusters, the Milky Way galaxy) has been interpreted as a possible secondary product of a decaying dark matter particle, though observations of the line have been controversial. If this interpretation of the 3.5 keV line is correct, there should exist a signal from the Milky Way that is correlated to models of the Milky Way galaxy’s dark matter distribution. HaloSat is an all-sky survey that, since October 2018, has been observing in the soft X-ray band from 0.4 – 7 keV. With its large field of view and significant coverage of the X-ray sky, HaloSat provides an opportunity to search for the 3.5 keV line originating from the Milky Way, which is the focus of my summer work.
Developing Tools for Examining Astronomical Outliers Identified with Machine Learning
Van Allen Summer Research Grant; Research Mentor: Prof. Casey DeRoo
Outliers within an astronomical catalog hold the potential to offer new insights into astrophysical phenomena. Watkins outlined her contributions to machine learning to identify outliers in the Chandra Source Catalog v2.0. I will explain a script I developed in Python to query the SIMBAD astronomical database for objects near our outlier coordinates. I will also talk about the issue and implications of missing data in our dataset. A follow-up study of the bias introduced by means of handling missing data has implications for future astronomical machine learning surveys.
Soft x-ray detection for small satellites with a commercial CMOS sensor
Steve Tammes & Tyler Roth
Research Mentors: Prof. Phil Kaaret, Prof. Casey DeRoo
Advancements in small satellite technology have enabled low cost x-ray astrophysics missions to be carried out on CubeSats. This has led to a demand for x-ray imaging instruments suitable to these mission budgets. We present results from our characterizations of a commercial CMOS sensor using the Advanced Photon Source synchrotron at Argonne National Laboratory.
High-Resolution NH3 Gas Temperatures in the Galactic Center Cloud
Mentors: Dr. Cornelia Lang & Dr. Natalie Butterfield (Green Bank Observatory)
We present high resolution (~3'', 0.1 pc) rotational and kinetic gas temperature measurements of the Galactic center cloud M0.10-0.08. The M0.10-0.08 cloud is a compact (~100'', 3 pc) cloud located ~25 pc in projection of the supermassive black-hole SrgA*. We derive gas temperatures throughout the cloud using multiple metastable transitions of NH3 taken with the Very Large Array (VLA). We used two methods to calculate the gas temperatures: a temperature map and a Boltzmann plot distribution. We compare our high-resolution temperature values with other surveys.