Realtime detection and characterization of binary black holes in LVK O4 using neural networks

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. At the time of writing 15 BBH events have already been detected in real-time by neural-network based search, Aframe, since late August 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 neural networks.