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 reanalyzed O3 data using both traditional and ML-based approaches. Here, we identify 38 candidates with a 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 unreported candidates are identified by Aframe. This work demonstrates that Aframe, and ML-based searches more generally, are useful companions to matched filtering pipelines.

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• Gravitational wave detection
• Gravitational waves
• Machine learning