Optimising Foreground Modelling for Global 21cm Cosmology with GPU-Accelerated Nested Sampling

TL;DR

This paper introduces a GPU-accelerated Bayesian inference framework for global 21-cm cosmology, significantly improving foreground modelling accuracy and efficiency, enabling robust detection of the early-Universe signal with reduced computational cost.

Contribution

It presents a novel GPU-accelerated nested sampling method with a new sky-partitioning scheme for improved foreground modelling in 21-cm cosmology.

Findings

Inference runtime reduced from hundreds of CPU-years to two GPU-days.

Sky-partitioning improves model accuracy and reduces parameters by 40%.

Enhanced recovery of spatially varying spectral indices.

Abstract

The global 21-cm signal provides a powerful probe of early-Universe astrophysics, but its detection is hindered by Galactic foregrounds that are orders of magnitude brighter than the signal and distortions introduced by beam chromaticity. These challenges require accurate foreground modelling, rigorous Bayesian model comparison, and robust validation frameworks. In this work, we substantially accelerate global 21-cm inference by exploiting GPU architectures, enabling likelihood evaluations to achieve near-constant wall-clock time across a wide range of model dimensionalities and data volumes. Combined with algorithmic parallelisation of Nested Sampling, this reduces the total inference runtime of this work from hundreds of CPU-years to approximately two GPU-days, corresponding to a cost reduction of over two orders of magnitude. Leveraging this capability, we advance the physically motivated forward-modelling approach, in which foregrounds are represented by a discrete set of sky regions by introducing a novel, observation-dependent sky-partitioning scheme that defines regions using the antenna beam-convolved sky power of a given observing window. We show that this scheme improves modelling performance in three ways: firstly, by enforcing a strictly nested region hierarchy that enables clear identification of the Occam penalty in the Bayesian evidence, facilitating principled optimisation of model complexity; secondly, by enabling more accurate recovery of spatially varying spectral indices, with posterior estimates centred within physically plausible ranges; and thirdly, by allowing complex foregrounds to be modelled for robust global 21-cm signal inference using substantially fewer parameters. Overall, this approach achieves validated recovery at lower region counts, corresponding to an approximate 40% reduction in foreground-model dimensionality.