Inferring the population properties of galactic binaries from LISA's stochastic foreground

TL;DR

This paper introduces a neural network-based framework to infer galactic binary population properties from LISA's stochastic gravitational-wave foreground, achieving accurate results and significant computational speed-up.

Contribution

A novel simulation-based inference method using neural posterior estimation to directly extract population parameters from LISA's foreground spectra.

Findings

Successfully recovered population parameters with good accuracy.

Achieved ~100X speed-up with GPU-accelerated subtraction algorithm.

Demonstrated the foreground's rich information content about galactic binaries.

Abstract

Galactic binaries are expected to be the most numerous LISA sources and to produce a stochastic gravitational-wave foreground whose spectral shape encodes information about the underlying population. Extracting this information with standard hierarchical methods is challenging due to the high dimensionality of the problem and the computational cost of global-fit analyses. We present a simulation-based inference framework to measure the population properties of galactic binaries directly from the reconstructed foreground. Adopting an astrophysically agnostic parametrization in the observable space -- defined by signal amplitude, frequency, and frequency derivative -- we generate synthetic catalogs and foreground spectra using a global-fit-inspired subtraction algorithm. We then train a neural posterior estimator to map spectra to population parameters. We validate our method on simulated data and recover population parameters with good accuracy, including the total number of binaries. As a by-product, we present a GPU-accelerated version of the subtraction algorithm, which delivers a ~100X speed-up compared to previous implementations in the literature. Our results demonstrate that LISA's stochastic foreground alone carries significant information about the Galactic binary population and provide a practical step toward joint inference from resolved and unresolved sources.