Core-collapse supernovae (CCSNe; SNe IIp) serve as the endpoints of the long lives of massive stars and the formative moments of young compact objects, such as black holes and neutron stars. In the data driven world of multimessenger astronomy, exploring how observations connect to theory-driven simulations is key to our understanding. I am currently developing a comprehensive statistical analysis of lightcurve models to investigate supernovae simulation accuracy with Carla Fröhlich at North Carolina State University and Brandon Barker at Los Alamos National Lab.

Using the stellar evolution models presented in Sukhbold et al. 2016, I have modeled early timescale CCSNe hydrodynamics using PUSH, calculated detailed nucleosynthesis for all exploding models using a 2000+ isotope nuclear reaction network, and generated post-collapse bolometric lightcurves using the SuperNova Explosion Code (SNEC). I am now exploring the connection between our simulations and observed lightcurves using the methodologies first used in Barker et al. 2022; 2023.

Stay tuned for two upcoming publications detailing this work!

Dark matter halos and their surrounding subhalo populations mirror the structure and evolution of galactic systems like our Milky Way and its nearby dwarf galaxies. Modeling how these structures change for different models of dark matter can better inform our understanding of cosmological theory. I investigated long-scale dark matter structure evolution using COZMIC, an N-body cosmological simulation suite, with Ethan Nadler and Vera Gluscevic through the Simons-NSBP Scholars Program at the Flatiron institute. I examined halo and subhalo mass distribution around Milky Way-like galaxies over long timescales, and produced first measurements of the time-dependence of the subhalo mass function for beyond-CDM models.

Predictive models for solar weather and activity rely on large amounts of data from extensive timescales, from one year to multiple 11-year-long solar cycles. I worked with Slava Sadykov at Georgia State University to support the development of more accurate prediction models for solar activity. Using magnetograms from the operational overlap between the Solar Dynamics Observatory (SDO/HMI) and Solar and Heliospheric Observatory (SOHO/MDI), I established machine learning models to enable the creation of a homogenous magnetic dataset spanning from 1994-present. These regression models resolved differences in instrument resolution, operational limitations, and observatory location. I also researched algorithm refinement and effectiveness on active regions and the quiet Sun.