# ★ Normalizing flows for complete parameter estimation on time-frequency representations of gravitational-wave data ★

- Institution: CERN
- Speaker: Daniel Lanchares
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025640)
- 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 speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this work we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work, we map BBH spectrograms from the Advanced LIGO and Advanced Virgo detectors to color channels in an RGB image amenable to be processed with residual networks. GP15 is trained on simulated data for BBH mergers obtained with the \texttt{IMRPhenomXPHM} waveform approximant and tested for all three-detector events from the GWTC-3 and GWTC-2.1 catalogs reported by the LIGO-Virgo-KAGRA (LVK) collaboration. Overall, our model yields good agreement with the LVK results over most parameters. Our simple model can produce large amounts of posterior samples in the order of a second, complementing existing approaches with normalizing flows based on time-only or frequency-only representation of gravitational-wave data.

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## Lectures

1. [★ Normalizing flows for complete parameter estimation on time-frequency representations of gravitational-wave data ★](https://invisiblenetwork.ai/learn/cern/3025640/5f133446ccbc), 20 min (CERN's player)