Avoiding selection bias in gravitational wave astronomy

TL;DR

This paper presents a method to avoid selection bias in gravitational wave data analysis by utilizing full information from the search, leading to unbiased parameter estimates even with false alarms and incomplete data.

Contribution

It introduces an approach that considers all data and models to eliminate bias caused by detection thresholds in gravitational wave searches.

Findings

Unbiased parameter estimation is achievable with full data consideration.

Lowering detection thresholds can improve inference quality.

Method remains effective despite high false alarm rates.

Abstract

When searching for gravitational waves in the data from ground-based gravitational wave detectors it is common to use a detection threshold to reduce the number of background events which are unlikely to be the signals of interest. However, imposing such a threshold will also discard some real signals with low amplitude, which can potentially bias any inferences drawn from the population of detected signals. We show how this selection bias is naturally avoided by using the full information from the search, considering both the selected data and our ignorance of the data that are thrown away, and considering all relevant signal and noise models. This approach produces unbiased estimates of parameters even in the presence of false alarms and incomplete data. This can be seen as an extension of previous methods into the high false rate regime where we are able to show that the quality of parameter inference can be optimised by lowering thresholds and increasing the false alarm rate.