Simulation-based inference for stochastic gravitational wave background data analysis

TL;DR

This paper demonstrates that simulation-based inference, specifically TMNRE, can effectively reconstruct stochastic gravitational wave backgrounds from simulated LISA data, offering advantages over traditional methods like MCMC.

Contribution

The paper introduces TMNRE for SGWB data analysis, showing its ability to reproduce traditional results and handle complex, low SNR signals efficiently.

Findings

TMNRE accurately reproduces traditional MCMC results.

TMNRE effectively marginalizes over complex parameter spaces.

The approach is validated on simulated LISA data with injected signals.

Abstract

The next generation of space- and ground-based facilities promise to reveal an entirely new picture of the gravitational wave sky: thousands of galactic and extragalactic binary signals, as well as stochastic gravitational wave backgrounds (SGWBs) of unresolved astrophysical and possibly cosmological signals. These will need to be disentangled to achieve the scientific goals of experiments such as LISA, Einstein Telescope, or Cosmic Explorer. We focus on one particular aspect of this challenge: reconstructing an SGWB from (mock) LISA data. We demonstrate that simulation-based inference (SBI) - specifically truncated marginal neural ratio estimation (TMNRE) - is a promising avenue to overcome some of the technical difficulties and compromises necessary when applying more traditional methods such as Monte Carlo Markov Chains (MCMC). To highlight this, we show that we can reproduce results from traditional methods both for a template-based and agnostic search for an SGWB. Moreover, as a demonstration of the rich potential of SBI, we consider the injection of a population of low signal-to-noise ratio supermassive black hole transient signals into the data. TMNRE can implicitly marginalize over this complicated parameter space, enabling us to directly and accurately reconstruct the stochastic (and instrumental noise) contributions. We publicly release our TMNRE implementation in the form of the code saqqara.