Speaker
Description
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.