The formation and evolution of binaries of compact astrophysical objects (black holes and neutron stars) is an important and challenging problem in astrophysics. Observational data of such systems comes from radio telescope detections of binaries with at least one pulsating neutron star, or pulsar, and from gravitational-wave observations of the final few seconds to minutes of the lives of the binary, when they inspiral and merge. A research collaboration between Johns Hopkins University and Pennsylvania State University will use gravitational-wave observations to explore astrophysical models of compact binaries, using statistical inference and machine learning. Among the specific scientific topics to be addressed are the details of how compact binaries form, the use of compact binary mergers to measure distances to sources far off in the universe, and understanding the state of matter in the dense cores of neutron stars. The phenomenal discoveries by the gravitational-wave detectors have received a great deal of attention from the press and in social media. Black holes, neutron stars and gravitational waves are topics of great interest to the public, and have the potential to attract students in middle and high schools to STEM subjects who would have otherwise chosen other alternatives. The researchers will use the current surge of interest in these areas to inform middle and high students of the excitement of research and discovery in the forefront of physics.
The proposed research will shed light on evolutionary models of compact binaries using a catalog of binary merger events observed by the LIGO and Virgo gravitational-wave detectors. More specifically, the goal of the project is to discriminate between competing evolutionary models of compact binaries (dynamical interactions in clusters, evolution of isolated binaries, and primordial origin of binary black holes) from the measured distributions of masses, spins and other properties of compact object mergers. This study will help produce a map from the astrophysical models to the bulk properties of the systems measured from gravitational wave observations. Such maps, in combination with Bayesian inference and machine learning, will be used to gain a deeper understanding of the astrophysics of compact object binaries. The investigators will use compact binaries as accurate measures of distance to obtain peculiar velocities of galaxies and clusters, 3-d mapping of galaxies, and to characterize the gas, stellar and dark matter content of galaxies, comparing the results with other approaches. Gravitational wave observations are key to multi-messenger astronomy, which will help address the origin questions, such as how and when black holes formed and how they evolved, what constitutes the expansion history of the Universe, and what is the state of matter in dense cores of neutron stars. This project advances the goals of the NSF Windows on the Universe Big Idea.
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.
