Astrophysics research at UTB involves modeling populations of compact object binaries in the Galaxy and its environs in order to determine how gravitational wave observations can shed light on models of stellar evolution and Galactic structure. This research is relevant to gravitational wave observations using LISA, LIGO/Virgo, and the Pulsar Timing Array.
Students working in my research: Frank Ceballos (undergraduate), Jose McKinnon (graduate)
Research Projects for future Graduate Students
Mock LISA Data Challenge Assessment: The Mock LISA Data Challenges are semi-realistic data sets that are distributed to data analysis groups around the world. At the end of the trial period, these groups return the results of their analysis algorithms to the MLDC taskforce. A student working on this project would develop methods to assess the ability of the returned data analysis results to answer the science questions for LISA. There are several separate projects as part of this project.
Intermediate Mass Black Holes: Use stellar evolution models to determine the mass loss rate from hyper-massive stars and develop and combine with mass gain rate from collisions through N-body simulations to determine likely final masses of intermediate mass black hole progenitors. This project can be broken into several small projects (e.g.: mass loss rate, N-body simulations, etc.)
Pulsars in the Galactic Center: Model the evolution of high mass stars in the Galactic center and calculate the population density of pulsars.
Research Projects for future Undergraduate Students
Binary Population Synthesis: Students will work on a project involving data mining or resampling from the results of binary population synthesis models produced by the StarTrack population synthesis code. The project will be centered on likely gravitational wave sources for either space- or ground-based interferometers. The data mining project will involve identifying specific evolutionary channels that produce gravitational wave sources while the resampling work will involve using the population synthesis output to generate multiple realizations of likely source populations.