Compact binary mergers produce gravitational waves (GWs), which carry information about their sources. The LIGO-VIRGO-KAGRA network detects GWs signals, and we use those measurements to infer BBHs population properties such as mass, spin, and eccentricity distribution in the universe. Binary component masses and spins are well-known to provide insight into the source formation, evolution, and environment. Unlike these two, eccentricity is a unique and measurable signature of strongly interacting events. Unfortunately, most available parameter estimation results for GWs signals do not include eccentricity: models for binary merger with eccentricity have minimal availability and parameter coverage. Therefore, to assess how well the eccentricity distribution can be recovered, we generated a synthetic population of non-spinning, non-precessing, lower mass (10M⊙ − 50M⊙), eccentric binary black holes (EBBHs) using a modified power law model and consist of 100 events with eccentricity distribution σε = 0.05. Furthermore, to compare the synthetic EBBHs population with the non-eccentric BBHs (NEBBHS), we estimate how our sources would be characterized by parameter inferences that omitted the effects of eccentricity. We apply Markov Chain Monte Carlo (MCMC) method to constrain model parameters: event rate R, α, mmin, mmax, and σε. Our method effectively recovered the eccentricity distribution de- spite our injections having low eccentricity. We also show that population parameters are better constrained with the eccentric population model than with the non-eccentric model.
Library of Congress Subject Headings
Black holes (Astronomy)--Mathematical models; Double stars--Mathematical models
Astrophysical Sciences and Technology (MS)
Department, Program, or Center
School of Physics and Astronomy (COS)
Zeeshan, Muhammad, "Population Inference of Non-Spinning Eccentric Binary Black Holes" (2023). Thesis. Rochester Institute of Technology. Accessed from
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