Eccentricity-induced systematic error on parametrized tests of general relativity: Hierarchical Bayesian inference applied to a binary black hole population
Pankaj Saini, Sajad A. Bhat, Marc Favata, K. G. Arun

TL;DR
This paper investigates how unmodeled binary eccentricity can cause systematic errors in testing general relativity with gravitational wave data, and uses hierarchical Bayesian inference to quantify these biases and improve bounds on deviations.
Contribution
It introduces a hierarchical Bayesian framework to assess eccentricity-induced biases in parametrized tests of GR across a BBH population.
Findings
Systematic biases from eccentricity can mimic false GR violations.
Projected bounds on TGR parameters are slightly worsened by eccentricity inclusion.
Hierarchical Bayesian inference effectively models population-level effects.
Abstract
One approach to testing general relativity (GR) introduces free parameters in the post-Newtonian (PN) expansion of the gravitational-wave (GW) phase. If systematic errors on these testing GR (TGR) parameters exceed the statistical errors, this may signal a false violation of GR. Here, we consider systematic errors produced by unmodeled binary eccentricity. Since the eccentricity of GW events in ground-based detectors is expected to be small or negligible, the use of quasicircular waveform models for testing GR may be safe when analyzing a small number of events. However, as the catalog size of GW detections increases, more stringent bounds on GR deviations can be placed by combining information from multiple events. In that case, even small systematic biases may become significant. We apply the approach of hierarchical Bayesian inference to model the posterior probability distributions…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Particle physics theoretical and experimental studies
