Research

With hundreds of gravitational-wave detections on hand, we now have sufficient data to conduct a large-scale census of the black hole and neutron star population, asking questions like:

• What is the overall distribution of compact object masses?
• How rapidly do compact objects spin, and in which directions?
• And how does the prevalence of compact binary mergers evolve over cosmic time?

These and other questions are answered via careful statistical inference with large, noisy, and incomplete catalogs of gravitational-wave data.

For every nearby and loud gravitational-wave source, there are a multitude more in the distant Universe. Although too quiet to individually detect, when taken together these objects yield a collective gravitational-wave hum: a stochastic background. The stochastic background is typically searched for in the form of a slight excess of correlated noise between widely separated detectors. Its detection (or even nondetection) can tell us about the nature of black holes at faraway corners of the Universe in the far distant past.

Although we now have observations of many hundreds of black hole mergers, the astrophysical environments in which these black holes form and the processes driving them to merger remain unknown. By studying the observed properties of merging black holes, either the characteristics of individual outliers or the collective traits of the black hole population, we can try to learn where and how black holes are born, the details of their environments, and maybe even something about their stellar progenitors.

Gravitational-wave data analysis is challenging: we can make only noisy measurements of an incomplete and biased slice of an underlying unknown astrophysical population. As the number of gravitational-wave detections continues to balloon, new data analysis and inference methods prove key to extracting all available information from measured data.