Speaker
Description
The LISA space mission, set to launch in the mid 2030s, will open a new window on the “gravitational wave universe”. Thanks to its exceptional sensitivity in the low frequency band ~10⁻⁴–10⁻¹ Hz, it will observe a variety of sources all at the same time: from massive black hole binaries to extreme mass ratio inspirals and Galactic compact binaries. Among these, double white dwarf binaries, emitting nearly monochromatic signals, are expected to dominate the mHz band. Of the ~10⁷ binaries in the Milky Way, only a small fraction—ranging from tens to thousands—will be individually resolvable by LISA, while the unresolved population will produce a stochastic foreground (“confusion noise”) that must be accounted for in the analysis of other sources.
Different astrophysical formation and evolution channels are expected to leave distinct imprints on this confusion noise. However, extracting population-level information from this signal remains a challenging and computationally expensive task within the standard Global Fit framework.
In this work, we present a machine learning approach based on Simulation-Based Inference to directly link the confusion noise to the underlying astrophysical population. We develop fast simulators to efficiently generate synthetic catalog realizations, and train a Neural Posterior Estimator to learn the mapping from the observed foreground to the parameters describing the binary population. We show that this approach can efficiently recover key population properties, bypassing some of the limitations of traditional inference pipelines.
Our results highlight the potential of modern AI-driven inference methods for gravitational-wave data analysis, and represent a step towards integrating Simulation-Based Inference within the LISA Global Fit framework.
This work is presented in detail in arXiv:2602.18560