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The observed distributions of the source properties from gravitational-wave detections are biased due to the selection effects and detection criteria in the detections, analogous to the Malmquist bias. In this work, this observation bias is investigated through its fundamental statistical and physical origins. An efficient semi-analytical formulation for its estimation is derived which is as accurate as the standard method of numerical simulations, with only a millionth of the computational cost. Then, the estimated bias is used for model independent inferences on the binary black hole population. These inferences show additional structures, specifically two potential mass gaps in the joint mass distribution, which were not found via modelled inferences. Example ready-to-use scripts and some produced datasets for this method are shared in an online repository.
We show that for detections of gravitational-wave transients, constraints can be given on physical parameters of the source without using any specific astrophysical models. Relying only on fundamental principles of general relativity, we can set uppe
Gravitational wave astronomy has established its role in measuring the equation of state governing cold supranuclear matter. To date and in the near future, gravitational wave measurements from neutron star binaries are likely to be restricted to the
With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly
We describe a Bayesian formalism for analyzing individual gravitational-wave events in light of the rest of an observed population. This analysis reveals how the idea of a ``population-informed prior arises naturally from a suitable marginalization o
We present a method for assigning a statistical significance to detection candidates in targeted searches for continuous gravitational waves from known pulsars, without assuming the detector noise is Gaussian and stationary. We take advantage of the