# Tackling the LISA Global Fit: Scalable Simulation-Based Inference and the Road Ahead

- Institution: CERN
- Speaker: James Alvey
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025584)
- 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 Laser Interferometer Space Antenna (LISA) will deliver an unprecedented view of the gravitational-wave universe, but unlocking its scientific potential hinges on a monumental data science challenge: the "global fit". Extracting thousands of overlapping, time-varying signals from complex instrumental noise is a high-dimensional inference problem that pushes the limits of traditional stochastic sampling methods. In this talk, I will explore how Simulation-Based Inference (SBI) is emerging as a promising framework to break this computational bottleneck. I will overview the current landscape of SBI applied to LISA data, highlighting how it has been used across the various source classes. Finally, I will outline what I see as the significant hurdles that remain as we scale these algorithms toward a full analysis pipeline.

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## Lectures

1. [Tackling the LISA Global Fit: Scalable Simulation-Based Inference and the Road Ahead](https://invisiblenetwork.ai/learn/cern/3025584/39fc9c7d2c97), 23 min (CERN's player)