On the reliability of parameter estimates in the first observing run of Advanced LIGO

TL;DR

This study evaluates the accuracy of Bayesian parameter estimation in Advanced LIGO's first observing run, confirming the Gaussian noise assumption's validity and highlighting challenges with certain sampling methods.

Contribution

It provides empirical validation of Gaussian noise assumptions in real detector data and compares the performance of different sampling algorithms in parameter estimation.

Findings

Unbiased parameter estimates are achievable in real detector data.

Sampling algorithms struggle with certain signals, emphasizing the need for targeted validation.

Gaussian noise assumption holds well for the analyzed data.

Abstract

Accurate parameter estimation is key to maximizing the scientific impact of gravitational-wave astronomy. Parameters of a binary merger are typically estimated using Bayesian inference. It is necessary to make several assumptions when doing so, one of which is that the the detectors output stationary Gaussian noise. We test the validity of these assumptions by performing percentile-percentile tests in both simulated Gaussian noise and real detector data in the first observing run of Advanced LIGO (O1). We add simulated signals to 512s of data centered on each of the three events detected in O1 -- GW150914, GW151012, and GW151226 -- and check that the recovered credible intervals match statistical expectations. We find that we are able to recover unbiased parameter estimates in the real detector data, indicating that the assumption of Gaussian noise does not adversely effect parameter estimates. However, we also find that both the parallel-tempered sampler emcee_pt and the nested sampler dynesty struggle to produced unbiased parameter estimates for GW151226-like signals, even in simulated Gaussian noise. The emcee_pt sampler does produce unbiased estimates for GW150914-like signals. This highlights the importance of performing percentile-percentile tests in different targeted areas of parameter space.