Current Research
Dark Matter Subhaloes:
Predicting dark subhalo populations with galaxy simulations
The nature of dark matter is currently unknown. One method of constraining its properties is to determine the lower mass limit of the gravitationally bound ‘clumps’ it can form—a quantity determined by the particle properties of dark matter. Some of these clumps, known as “subhalos” of a galaxy’s primary dark matter halo, have been observed as components of small, satellite galaxies. Some particle models of dark matter predict these to be the smallest possible subhalos; others predict even smaller subhaloes that contain no stars, making them difficult to detect. To support observational campaigns to detect these objects, I examined Milky Way-mass galaxies in the FIRE simulations to set expectations for dark subhalo populations within CDM. I also used our results to demonstrate that the LMC’s recent close pass has likely enriched the inner galaxy with extra subhalos.
Publication in prep
Past Research
Megan L. Barry 2019
Neutron Star Oscillations:
Identifying the Quark-Hadron Phase Transition
The field of seismology describes how various types of oscillations travel through the Earth. It is predicted that similar oscillations occur in neutron stars, and that the frequency of these oscillations is related to the stars’ composition. At CSU Long Beach, I studied under Dr. Prashanth Jaikumar to complete my Master’s thesis on g-modes, oscillations that occur as equilibrium is restored to a displaced fluid parcel within the star. We found that neutron stars with a quark core had noticeably higher g-mode frequencies, offering a diagnostic for the presence of a quark-hadron phase transition. This research was sponsored by a grant from the National Science Foundation.
Fermion Thermodynamics:
Investigating Alternate Distribution Functions
As a recipient of the 2019 Summer Research Assistantship at CSULB, I worked on a project under Dr. Thomas Klähn, with fellow student Mohammed Kahn, in which we investigated the impact of using the Tsallis distribution to model the behavior of fermions in extreme astrophysical environments, inspired by its accuracy in modeling particle distributions in heavy ion collisions. We computed the values of physical parameters at different temperatures and chemical potentials using both the Tsallis and Fermi-Dirac distribution. At low temperatures and densities, such as those found in the circumburst medium of a supernova, calculations of sound speed using the Tsallis distribution show signs of a phase transition that is not seen in the Fermi-Dirac results. Thus, the Tsallis distribution provides distinct and potentially more accurate predictions in some circumstances.
Skills used: numerical integration, quantum statistics, Fortran