Probing Binary Black Hole Formation Channels with Gravitational Waves

This dissertation presents the work I've done with gravitational waves using data from and as part of the LIGO-Virgo-KAGRA collaboration. The work in this dissertation showcases different approaches to analyzing the data in the third gravitational wave transient catalog. Since all of the detected signals in the catalog are consistent with compact binary coalescences, most of them binary black holes, it provides a great window to peek into the black hole demographics of the universe. The parametric noise modeling algorithm was introduced to conduct joint inference on signal parameters and noise parameters, enabling probes into hidden correlations between signal and noise models. Several clustering algorithms were developed to find patterns in gravitational-wave data which have significant uncertainties, using which, a lot of the patterns found in other population analyses were recovered and a few outliers were identified. The coagulation model for hierarchical mergers was refined and constrained with the catalog, providing evidence for the massive event GW190521 being a hierarchical merger very likely produced in an active galactic nucleus disk. All the techniques established in this dissertation will prove useful in analyzing the rapidly growing gravitational-wave census, thereby improving our understanding of black holes as well as the universe at large.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Department of Physics

Grantor dc:publisher

University of Oregon

Year dc:date.issued

2025

Author dc:creator

• Paul, Sangeet

Advisor dc:contributor.advisor

• Farr, Ben

• black holes
• gravitational waves

Statement dc:rights

• CC BY-NC-ND

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1794/31894

OAI record oai:identifier

https://scholarsbank.uoregon.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Ascholarsbank.uoregon.edu%3A1794%2F31894