Realtime search and inference of binary black holes in LVK data using AI

          @misc{ scivideos_oai:cds.cern.ch:3025607,
            doi = {},
            url = {https://videos.cern.ch/record/3025607},
            author = {},
            keywords = {},
            language = {en},
            title = {Realtime search and inference of binary black holes in LVK data using AI},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025607 see, \url{https://scivideos.org/cern-cds/3025607}}
          }
          

Abstract

The number of GW events have increased from two real-time detections in the LIGO first observing run, to over two hundred in the LIGO-Virgo-KAGRA fourth observing run. In parallel, the last decade has also seen the increased use of machine learning, especially neural networks, in science. For the first time, after a decade of discovery, binary black holes (BBHs) are routinely detected by neural-networks as a part of the LVK data analysis. A total of 23 BBH events were detected in real-time by neural-network based search, Aframe, between late August to mid November 2025. Sky-localization and chirp mass estimates have been distributed in low latency using neural network based parameter estimation algorithm AMPLFI. I will be talking about Aframe and AMPLFI with a view toward this new paradigm of doing low latency GW science using AI.

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