Binary Black Hole Mergers from LIGO/Virgo O1 and O2: Population Inference Combining Confident and Marginal Events
Javier Roulet, Tejaswi Venumadhav, Barak Zackay, Liang Dai, Matias, Zaldarriaga

TL;DR
This paper performs a comprehensive statistical analysis of binary black hole mergers from LIGO/Virgo data, inferring population properties and merger rates while accounting for event significance and background noise.
Contribution
It introduces a novel formalism for consistent population inference from events of varying significance and combines data to constrain black hole mass, spin, and rate distributions.
Findings
Mass distribution cutoff at ~41 solar masses.
Mass ratio favors near-equal masses.
Merger rate between 1.5 and 5.3 Gpc^{-3} yr^{-1} at z~0.2.
Abstract
We perform a statistical inference of the astrophysical population of binary black hole (BBH) mergers observed during the first two observing runs of Advanced LIGO and Advanced Virgo, including events reported in the GWTC-1 and IAS catalogs. We derive a novel formalism to fully and consistently account for events of arbitrary significance. We carry out a software injection campaign to obtain a set of mock astrophysical events subject to our selection effects, and use the search background to compute the astrophysical probabilities of candidate events for several phenomenological models of the BBH population. We emphasize that the values of depend on both the astrophysical and background models. Finally, we combine the information from individual events to infer the rate, spin, mass, mass-ratio and redshift distributions of the mergers. The existing…
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