Accelerated inference of binary black-hole populations from the stochastic gravitational-wave background

TL;DR

This paper introduces a neural network-based method to rapidly infer properties of binary black-hole populations from the stochastic gravitational-wave background, leveraging importance sampling and accounting for intrinsic variance, thus enabling efficient analysis with third-generation detectors.

Contribution

The authors develop a neural network approach combined with importance sampling to accelerate population inference from the SGWB, including intrinsic variance, which was previously computationally prohibitive.

Findings

Neural networks can effectively interpolate the SGWB model.

The method significantly reduces inference time compared to traditional techniques.

Including intrinsic variance improves the accuracy of population parameter estimates.

Abstract

Third-generation ground-based gravitational wave detectors are expected to observe $\mathcal{O}(10^5)$ of overlapping signals per year from a multitude of astrophysical sources that will be computationally challenging to resolve individually. On the other hand, the stochastic background resulting from the entire population of sources encodes information about the underlying population, allowing for population parameter inference independent and complementary to that obtained with individually resolved events. Parameter estimation in this case is still computationally challenging, as computing the power spectrum involves sampling $\sim 10^5$ sources for each set of hyperparameters describing the binary population. In this work, we build on recently developed importance sampling techniques to compute the SGWB efficiently and train neural networks to interpolate the resulting background. We show that a multi-layer perceptron can encode the model information, allowing for significantly faster inference. We test the network assuming an observing setup with CE and ET sensitivities, where for the first time we include the intrinsic variance of the SGWB in the inference, as in this setup it presents a dominant source of measurement noise.