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

- Institution: CERN
- Speaker: Deep Chatterjee
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025607)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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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## Lectures

1. [Realtime search and inference of binary black holes in LVK data using AI](https://invisiblenetwork.ai/learn/cern/3025607/39b113da1959), 1 h 14 min (CERN's player)