A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run

TL;DR

This paper presents a machine learning method called Aframe for detecting binary black hole mergers in LIGO-Virgo-KAGRA data, showing it can identify known candidates and serve as a useful complement to traditional search techniques.

Contribution

The paper introduces Aframe, a novel ML-based search approach for gravitational-wave data, demonstrating its effectiveness alongside existing matched filtering methods.

Findings

Aframe identified 38 candidates with high astrophysical probability, matching previous reports.

Aframe found 3 additional candidates not previously reported.

Aframe is a valuable complementary tool to traditional matched filtering searches.

Abstract

We conduct a search for stellar-mass binary black hole mergers in gravitational-wave data collected by the LIGO detectors during the LIGO-Virgo-KAGRA (LVK) third observing run (O3). Our search uses a machine learning (ML) based method, Aframe, an alternative to traditional matched filtering search techniques. The O3 observing run has been analyzed by the LVK collaboration, producing GWTC-3, the most recent catalog installment which has been made publicly available in 2021. Various groups outside the LVK have re-analyzed O3 data using both traditional and ML-based approaches. Here, we identify 38 candidates with probability of astrophysical origin ($p_\mathrm{astro}$) greater than 0.5, which were previously reported in GWTC-3. This is comparable to the number of candidates reported by individual matched-filter searches. In addition, we compare Aframe candidates with catalogs from research groups outside of the LVK, identifying three candidates with $p_\mathrm{astro} > 0.5$. No previously un-reported candidates are identified by Aframe. This work demonstrates that Aframe, and ML based searches more generally, are useful companions to matched filtering pipelines.