Abstract

In order to extract information about the properties of compact binaries, we must estimate the noise power spectral density of gravitational-wave data, which depends on the properties of the gravitational-wave detector. In practice, it is not possible to know this perfectly, only to estimate it from the data. Multiple estimation methods are commonly used and each has a corresponding statistical uncertainty. However, this uncertainty is widely ignored when measuring the physical parameters describing compact binary coalescences, and the appropriate likelihoods which account for the uncertainty are not well known. In order to perform increasingly precise astrophysical inference and model selection, it will be essential to account for this uncertainty. In this work, we derive the correct likelihood for one of the most widely used estimation methods in gravitational-wave transient analysis, the median average. We demonstrate that simulated Gaussian noise follows the predicted distributions. We then examine real gravitational-wave data at and around the time of GW151012, a relatively low-significance binary black hole merger event. We show that the data are well described by stationary-Gaussian noise and explore the impact of different noise power spectral density estimation methods on the astrophysical inferences we draw about GW151012.

Highlights

  • The astrophysical parameters of compact binaries are inferred from gravitational-wave data using Bayesian inference

  • We show the natural logarithm of the BCI under these four set of assumptions. We find that both power spectral density (PSD) estimation methods have ln BCI ≈ 10 which is a moderately strong preference for the coherent hypothesis, we note that a full treatment requires careful consideration of prior odds

  • Performing astrophysical inference on gravitational-wave data requires an estimate of the noise power spectral density (PSD)

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Summary

INTRODUCTION

The astrophysical parameters of compact binaries are inferred from gravitational-wave data using Bayesian inference. We demonstrate that data whitened with a median PSD estimate follows a different distribution, and we show how to marginalize over the uncertainty in this estimated PSD to obtain the correct likelihood for stationary, Gaussian noise. We analyze the marginal gravitational-wave candidate GW151012 with both mean and median PSD estimates to understand the effect of marginalizing over the statistical uncertainty and of using the different estimation techniques This event is convenient for our present purposes since the effects we seek to study are most prominent for marginal signals like GW151012.

Gaussian noise
T Pi exp
Median PSD estimate
Mean PSD estimate
Limiting cases
BAYESIAN INFERENCE FOR GRAVITATIONAL-WAVE
DEMONSTRATION WITH GAUSSIAN NOISE
A CASE STUDY
Data quality tests
Data whitening
Impact on inference
DISCUSSION
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