Unlocking Supernova Secrets with Machine Learning and Gravitational Waves

Faculty Development Competitive Research Grants Program 2025-2027

Core-collapse supernovae are the powerful explosions at the end of massive star lives. We study the possibility of measuring the parameters of the equation of state (EOS) of high-density nuclear matter from the emitted gravitational wave (GW) signals. We focus on the bounce and early post-bounce signal from rapidly rotating models, which, despite their rarity, can be efficiently modeled with modest computational resources, allowing extensive application of machine learning methods. We generate GW data via computer simulations and then perform EOS parameter estimation by applying machine learning and statistical methods. The project will be conducted mainly by graduate students under guidance of the PI. The requested funds will mostly cover the students’ salaries, travel expenses, and publication fees.