Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts
Date of Award
Doctor of Philosophy
David Kaplan, Daniel Agterberg, Philip Chang, Sarah Vigeland
Black holes, Gravitational-waves, Hierarchical Bayesian inference, Kilonovae, Neutron stars, Population inference
Gravitational-wave (GW) astronomy has presented an unprecedented way to view the universe and study populations of astrophysical objects such as merging compact binaries containing black holes (BHs) and neutron stars (NSs). With the latest catalog of observations detected by the Advanced LIGO-Virgo detector network, recent analyses are placing interesting constraints on the population of BHs and NSs in these binaries. In particular, we are learning a great deal about how these binaries are distributed as a function of their masses. Another aspect of GW astronomy that has the potential to provide insights into fundamental physics is the multi-messenger follow up of the potential "kilonova" from binary mergers involving NSs. Observations or non-detections of kilonovae can be used to learn more about the formation of heavy elements via r-process nucleosynthesis as well as to shed light on the inner mechanisms of such mergers. This dissertation presents two studies that focus on inferring population properties from compact binaries using data-driven methods. The first is using the flexible approach of Gaussian processes to model the mass distribution of compact binaries and the second is developing a hierarchical Bayesian inference framework to infer kilonova population properties using non-detections in electromagnetic surveys.
Mohite, Siddharth, "Data-Driven Population Inference from Gravitational-Wave Sources and Electromagnetic Counterparts" (2022). Theses and Dissertations. 2926.