NSF Award
2409736
Gravitational-wave observations of neutron star mergers lead to unprecedented insight into the physics of neutron stars and the properties of dense matter. Measuring stellar properties, such as tidal deformations, across a range of neutron-star masses will map the pressure-density connection within the stars: the neutron star equation of state. The work supported by this award will explore the data science problem of equation-of-state inference using gravitational-wave observations, and its integration with knowledge of other astronomical and terrestrial messengers of dense neutron-rich matter. It will develop the community’s ability to account for uncertainties in the source models, as systematic error from waveform modeling is likely to dominate the inference of matter properties as detector network sensitivity increases. Finally, this work will apply uncertainty quantification methods to develop goals for interferometer commissioning and instrumental design, showing how improvements in various frequency ranges will translate into improved science potential. The results will drive design requirements for next-generation facilities to answer key science questions in nuclear astrophysics.<br/><br/>This work will engage undergraduate and master’s degree students at California State University Fullerton in developing and applying modern methods of statistical learning and uncertainty quantification to understand the nuclear astrophysics implications of current and future-generation gravitational-wave observations. As members of the Extreme Matter group in the LIGO Scientific Collaboration, the PI's team will develop inference tools that apply a modern statistical learning framework for equation-of-state inference. The team will connect neutron star-related science goals to frequency-dependent requirements on (1) the accuracy of source models, (2) the detector-network sensitivity and commissioning goals, and (3) the modeling and calibration of the instrumental response. These projects will offer gravitational-wave astronomy as a playground for computational and statistical exploration. Integrating data science and gravitational-wave astronomy will enrich mathematics and physics education at CSUF, recruiting future leaders in gravitational-wave astronomy and launching students toward fulfilling careers in science and technology.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
