The astrophysical odds of GW151216

TL;DR

This paper evaluates the likelihood that the gravitational-wave candidate GW151216 is an actual astrophysical event, using a Bayesian method that considers detector coherence and noise models, and finds it unlikely to be genuine.

Contribution

It introduces an improved Bayesian approach to assess the astrophysical nature of gravitational-wave candidates, refining the probability estimates compared to previous methods.

Findings

GW151216 has a low probability ($p_{astro}=0.03$) of being an astrophysical signal.

GW150914 and GW151012 are confirmed as highly likely astrophysical events with $p_{astro}$ of 1 and 0.997.

The new method enhances the discrimination between true signals and noise in gravitational-wave data.

Abstract

The gravitational-wave candidate GW151216 is a proposed binary black hole event from the first observing run of the Advanced LIGO detectors. Not identified as a bona fide signal by the LIGO--Virgo collaboration, there is disagreement as to its authenticity, which is quantified by $p_\text{astro}$, the probability that the event is astrophysical in origin. Previous estimates of $p_\text{astro}$ from different groups range from 0.18 to 0.71, making it unclear whether this event should be included in population analyses, which typically require $p_\text{astro}>0.5$. Whether GW151216 is an astrophysical signal or not has implications for the population properties of stellar-mass black holes and hence the evolution of massive stars. Using the astrophysical odds, a Bayesian method which uses the signal coherence between detectors and a parameterised model of non-astrophysical detector noise, we find that $p_\text{astro}=0.03$, suggesting that GW151216 is unlikely to be a genuine signal. We also analyse GW150914 (the first gravitational-wave detection) and GW151012 (initially considered to be an ambiguous detection) and find $p_\text{astro}$ values of 1 and 0.997 respectively. We argue that the astrophysical odds presented here improve upon traditional methods for distinguishing signals from noise.