Bahamas: BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background

TL;DR

The paper introduces 'bahamas', a Bayesian inference code using Hamiltonian Monte Carlo for analyzing complex noise and stochastic gravitational wave backgrounds in LISA data, accommodating non-stationarities and data gaps.

Contribution

It presents a novel, flexible Bayesian inference framework with a time-frequency approach and efficient sampling for LISA noise and background characterization.

Findings

Demonstrated performance on a test case

Supports multiple SGWB spectral models

Enables joint inference of noise and signals

Abstract

The LISA datastream will be populated by large instrumental and astrophysical noises, both potentially exhibiting long-term non-stationarities. Modelling and inferring on them is a challenging task, central for accurate signal reconstruction. In this paper, we introduce $\texttt{bahamas}$, a codebase designed to characterize noises and stochastic gravitational wave backgrounds (SGWBs) in LISA. $\texttt{bahamas}$ adopts a time-frequency data representation, based on the Short Time Fourier Transform, to accurately describe the signal temporal evolution and accommodate for the presence of data gaps. In addition, $\texttt{bahamas}$ supports a variety of SGWB spectral models proposed in literature, enabling joint inference on them. Posterior sampling leverages No-U-Turn sampling an efficient variant of Hamiltonian Monte Carlo, inheriting the cross-hardware capabilities provided by NumPyro (CPU/GPU/TPU). We benchmark $\texttt{bahamas}$ performances on a simple test case, and present ongoing developments to appear in future releases.