This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. In a fraction of a second on September 14, 2015, the field of gravitational-wave astrophysics was born with the detection by the LIGO instruments of two black holes colliding over a billion lightyears away. Less than two years later, over the span of 12 hours on August 17, 2017, the field of gravitational- wave multi-messenger astronomy was born with the detection of both gravitational-waves and light from the collision of two neutron stars. Over the next years many additional collisions will be detected, and this project will explore what these sources will teach us about physics, astrophysics, and cosmology. The project focuses on learning about how the distribution of the masses of the detected systems help us understand how the Universe makes black holes and neutron stars. This relates to how the Universe makes stars, which in turn relates to how the universe makes the elements which are crucial to life. In addition to exploring these fascinating questions and publishing results to help move the field forward, the group will also perform extensive education and outreach activities related to gravitational-waves and black holes.
The project will analyze the source catalogs and determine underlying population distributions. The PI will examine the mass distribution of the component compact objects in the detected binaries. In particular, the PI will explore both the putative lower mass gap (between the most massive neutron star and the least massive black hole), as well as the upper mass gap (a dearth of black holes of mass 50 solar masses ). Similarly, the PI will explore the spin and mass ratio distributions of the population, as well as the redshift evolution of all of these distributions. Characterizing these distributions, and their evolution, will teach us about the formation and evolution of black holes and neutron stars, as well as binaries that contain them. As our understanding of the distributions improves, the ability to characterize outliers from these underlying distributions also improves. The PI will further study this process, both highlighting the nature of outliers, as well as developing population-based parameter estimation. Furthermore, the PI will study associated astrophysical constraints based on these distributions, including forward modeling of these detections using population synthesis, and machine learning approaches to the equation of state of neutron stars.
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.
