arXiv subjects

Joe H. N. Pattison

Publications and source records attributed to Joe H. N. Pattison.

6 recordsLinked to original sources

arXiv2026

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

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.

astro-ph.IM↗

arXiv2026

Optimisation of calibration sources for global 21-cm experiments: the REACH case

The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that could hinder a detection or lead to false inferences. REACH (Radio Experiment for the Analysis of Cosmic Hydrogen) is a radiometer experiment designed to detect this sky-averaged signal in the frequency range of 50--130~MHz. Using a wide-beam antenna, REACH calibration relies on internal reference sources, covering a broad range of temperatures and reflection coefficients. The choice of type and number of calibrators used significantly influences the quality of the calibration. This work investigates these effects and introduces a novel method for selecting an optimal set of calibration sources. With an optimised set, we aim to reduce calibration time, thereby increasing sky integration time while preserving calibration accuracy. We explore two optimisation strategies: one applied across the full receiver band and another performed on a frequency-by-frequency basis. Finally, we demonstrate that, with a total calibration time comparable to the conventional full-calibrator set, an optimised set with fewer calibrators achieves approximately a $15~\%$ reduction in calibrated temperature noise and improved absolute calibration of the instrument. This has implications for better calibration strategies in similar radiometer experiments.

astro-ph.IM↗

arXiv2026

Impact of Antenna Structure and Orientation on Forward-Modelled Global 21 cm Signal Recovery

The redshifted 21 cm absorption trough from cosmic atomic hydrogen is one of the most promising probes of the early Universe, but its detection is challenged by bright foregrounds and instrumental systematics. In this work we quantify the impact of antenna mismodelling on signal recovery within a fully Bayesian, forward-modelled data analysis pipeline. We show that discrepancies between simulated and modelled antenna beams lead to frequency dependent errors in antenna temperature that can bias parameter inference. In particular, we demonstrate that orientation mismatches at the level of 0.25 degrees can significantly bias recovered signal parameters in typical observing scenarios. However, we also show that Bayesian evidence can be used to infer antenna orientation within this precision by scanning over model realisations. For structural mismodelling, we find that broadband recovery of all signal parameters requires accurate beam knowledge, but that partial recovery remains possible. Signal frequency and width can be robustly recovered under restricted frequency bands even when the antenna structure is imperfectly modelled, but signal depth is highly sensitive to beam errors. These results quantify the level of beam knowledge required for forward-modelled global 21 cm experiments and highlight the importance of observing strategy and antenna design in mitigating beam-sky coupling systematics.

astro-ph.CO↗

arXiv2025

Quantifying the Impact of Lunar and Planetary Occultation on Experimental Global 21 cm Cosmology

The global 21 cm signal from the hyperfine transition of cosmic atomic hydrogen is theorised to track the state of the early Universe via the analysis of its absorption and emission with respect to the radio background. Detecting this signal has been a challenge for astronomers since it was first postulated due to the presence of strong galactic foregrounds obfuscating the transition. Forward modelling techniques that aim to simulate and then remove these foregrounds have been suggested as a workaround to this problem. This technique, however, requires a precise and accurate understanding of the foregrounds in question. As we move into the next major lunar standstill, the moon will be able to occult high power areas of the sky in ways that are unaccounted for by maps used to simulate these foregrounds, and thereby disrupt signal recovery. We show that in toy cases an occultation from the moon, or other proximate object, leading to a mismatch in our expected and actual foregrounds of 15 parts per million increases the error in signal recovery of up to 20\%. We show that this level of disruption can happen when the moon is found towards the centre of the galaxy and is amplified when this alignment happens at high directivity regions of the antenna beam, causing a disruption of up to 180 parts per million, leading to a signal recovery error of 115\%. This allows us to identify lunar alignment scenarios that should be actively avoided to preserve signal fidelity. We also demonstrate that a body with a smaller apparent size than the moon, such as Venus, is unlikely to cause any signal disruption due to occultation, giving a base map error of <2 parts per million.

astro-ph.IM↗

arXiv2024

Global 21 cm Signal Recovery Under Changing Environmental Conditions

The redshifted 21 cm line of cosmic atomic hydrogen is one of the most auspicious tools in deciphering the early Universe. Recovering this signal remains an ongoing problem for cosmologists in the field, with the signal being hidden behind foregrounds approximately five orders of magnitude brighter than itself. A traditional forward modelling data analysis pipeline using Bayesian data analysis and a physically motivated foreground model to find this signal shows great promise in the case of unchanging environmental conditions. However we demonstrate in this paper that in the presence of a soil with changing dielectric properties under the antenna over time, or a changing soil temperature in the far field of our observation these traditional methods struggle. In this paper we detail a tool using Masked Auto-regressive Flows that improves upon previous physically motivated foreground models when one is trying to recover this signal in the presence of changing environmental conditions. We demonstrate that with these changing parameters our tool consistently recovers the signal with a much greater Bayesian evidence than the traditional data analysis pipeline, decreasing the root mean square error in the recovery of the injected signal by up to 45 %.

astro-ph.CO↗

arXiv2023

Modelling a Hot Horizon in Global 21 cm Experimental Foregrounds

The 21 cm signal from cosmic hydrogen is one of the most propitious probes of the early Universe. The detection of this signal would reveal key information about the first stars, the nature of dark matter, and early structure formation. We explore the impact of an emissive and reflective, or `hot', horizon on the recovery of this signal for global 21 cm experiments. It is demonstrated that using physically motivated foreground models to recover the sky-averaged 21 cm signal one must accurately describe the horizon around the radiometer. We show that not accounting for the horizon will lead to a signal recovery with residuals an order of magnitude larger than the injected signal, with a log Bayesian evidence of almost 1600 lower than when one does account for the horizon. It is shown that signal recovery is sensitive to incorrect values of soil temperature and reflection coefficient in describing the horizon, with even a 10% error in reflectance causing twofold increases in the RMSE of a given fit. We also show these parameters may be fitted using Bayesian inference to mitigate for these issues without overfitting and mischaracterising a non-detection. We further demonstrate that signal recovery is sensitive to errors in measurements of the horizon projection onto the sky, but fitting for soil temperature and reflection coefficients with priors that extend beyond physical expectation can resolve these problems. We show that using an expanded prior range can reliably recover the signal even when the height of the horizon is mismeasured by up to 20%, decreasing the RMSE from the model that does not perform this fitting by a factor of 9.

astro-ph.CO↗