PIRSA:22010086

Gravitational-wave Source Inference with Data-driven Models

APA

Edelman, B. (2022). Gravitational-wave Source Inference with Data-driven Models. Perimeter Institute. https://pirsa.org/22010086

MLA

Edelman, Bruce. Gravitational-wave Source Inference with Data-driven Models. Perimeter Institute, Jan. 20, 2022, https://pirsa.org/22010086

BibTex

          @misc{ pirsa_22010086,
            doi = {},
            url = {https://pirsa.org/22010086},
            author = {Edelman, Bruce},
            keywords = {Strong Gravity},
            language = {en},
            title = {Gravitational-wave Source Inference with Data-driven Models},
            publisher = {Perimeter Institute},
            year = {2022},
            month = {jan},
            note = {PIRSA:22010086 see, \url{https://pirsa.org}}
          }
          

Abstract

With the release of the third gravitational wave transient catalog (GWTC-3), the LIGO and Virgo detectors have reported nearly 100 gravitational waves from colliding black holes and neutron stars. Among these detections there have been numerous surprises, such as the heavy GW190521, the confidently asymmetric GW190412, and the exceptionally small secondary of GW190814. In addition to analyses of each individual sources' properties, such as their masses and spins, one can also summarize the collective properties of the colliding objects as population probability distributions over these parameters. As catalog sizes continue to grow, it enables both finer grained investigations into the population properties of merging compact objects, and robustly testing GR in the strong gravity regime. In this talk I will present data driven statistical models to look for deviations to underlying theoretical expectations, both for individual gravitational waveform models and population models describing the astrophysical distributions of merging compact binaries. I will present the results of an analysis using this novel data-driven model on the 11 compact binary mergers in GWTC-1, then move towards hierarchical models, inferring the binary black hole mass distribution with similar data-driven methods. I will conclude with showing new results from the LVK population analyses of GWTC-3 and motivate the need towards developing more data-driven statistical models for the incoming swath of observations expected in the fourth observing run that, as we have seen, will likely continue to further challenge theoretical expectations.