Using gravitational waves to observe thermal effects during binary neutron star mergers

In a study published in Letters from the astrophysical journalthe researchers examined neutron star mergers using THC_M1, a computer code that simulates neutron star mergers and accounts for the curvature of spacetime, due to the stars’ strong gravitational field, and neutrino processes in dense matter.

The researchers tested thermal effects on fusion by varying the specific heat capacity in the equation of state, which measures the amount of energy needed to increase the temperature of neutron star matter by one degree. To ensure the robustness of the results, the researchers carried out simulations at two resolutions. They repeated the tests at higher resolution with more approximate neutrino processing.

When two neutron stars orbit each other, they release ripples in space-time called gravitational waves. These ripples sap energy from the orbit until the two stars eventually collide and merge into a single object. Scientists used supercomputer simulations to explore how the behavior of different models of nuclear matter affects the gravitational waves released after these mergers. They discovered a strong correlation between the temperature of the remains and the frequency of these gravitational waves. Next-generation detectors will be able to distinguish these models from each other.

Scientists use neutron stars as laboratories for nuclear matter in conditions impossible to probe on Earth. They use today’s gravitational wave detectors to observe neutron star mergers and discover the behavior of cold, ultra-dense matter. However, these detectors cannot measure the signal after the stars merge. This signal contains information about hot nuclear material.

Future detectors will be more sensitive to these signals. Because they will also be able to distinguish different models from each other, the results of this study suggest that the upcoming detectors will help scientists create better models for hot nuclear matter.

This work used computing resources available through the National Energy Research Scientific Computing Center, the Pittsburgh Supercomputing Center, and the Institute for Computational and Data Science at Pennsylvania State University.