arXiv subjects

Dominic J. Anstey

Publications and source records attributed to Dominic J. Anstey.

9 recordsLinked to original sources

arXiv2026

Towards end-to-end Bayesian forward models in global 21-cm cosmology: surrogate modelling and marginalisation of beam uncertainty

Robust statistical inference in global 21-cm cosmology requires end-to-end uncertainty quantification that jointly handles the highly degenerate cosmological signal, foreground emission, and instrumental response. Although electromagnetic simulations capture physical antenna properties in a parametrised way, multi-hour runtimes make their integration within likelihood-based sampling frameworks infeasible. Most existing approaches therefore assume a single precomputed beam, a fragile assumption given our demonstration that realistic mismatches can severely bias the recovered cosmological and foreground parameters. To address this, we present an accelerated and differentiable Bayesian framework that incorporates an informed surrogate representation of chromatic beam uncertainty directly into a forward-modelling pipeline. Treating the physical antenna properties as nuisance quantities, we apply a two-stage decomposition directly to simulated directivity patterns, reducing the instrumental parameterisation by two orders of magnitude while retaining the angular and spectral structure required for accurate beam reconstruction. Exploiting the linearity of the resulting surrogate, we use analytical marginalisation to allow the continuous instrumental uncertainty to be propagated into the final posteriors and Bayesian evidence without directly sampling the beam space. Testing the framework against a suite of unseen beams and cosmological signals, we recover the true inputs at approximately the instrumental-noise level. We further show that, for the uncertainty considered here, as few as 100 electromagnetic simulations are sufficient to construct an effective surrogate, substantially reducing the simulation burden for future analyses. This framework provides a scalable, statistically rigorous route towards hardware-accelerated uncertainty quantification in global 21-cm cosmology.

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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.

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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.

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arXiv2025

Capturing System Drift with Time Series Calibration for Global 21-cm Cosmology Experiments

To achieve the sensitivity required to detect signals from neutral hydrogen from the Cosmic Dawn and Epoch of Reionisation it is critical to have a well-calibrated instrument which has a stable calibration over the course of the observation. Previous calibration methods do not explicitly use the time information available and make assumptions on the impedance matching of the reference sources. Here we present a new calibration method based on noise wave parameters which fits a calibration solution over time and frequency to the data, interpolating the solutions to the times at which the antenna is being measured. To test this method we simulate a dataset using measurements of the REACH receiver, modelling a low noise amplifier which is drifting over time. Fitting a polynomial surface in frequency and time to the simulated data demonstrates that we can remove the drift in the calibrated solution over time but leaves a chromatic residual. We further show that we can remove assumptions on the reflection coefficients of the reference noise source and the cold load, reducing degeneracies in the parameter fits. Applying this new calibration equation and surface fitting method to the simulated data removes the chromatic residual in the calibrated spectrum and recovers the parameters to within 0.06% of the truth and a 97% reduction in the RMSE of the spectrum of the validation source compared with previous calibration methods. For two parameters we report up to six times smaller fit error after the degeneracies are removed from the time-based calibration.

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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.

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arXiv2025

Accounting for Noise and Singularities in Bayesian Calibration Methods for Global 21-cm Cosmology Experiments

Due to the large dynamic ranges involved with separating the cosmological 21-cm signal from the Cosmic Dawn from galactic foregrounds, a well-calibrated instrument is essential to avoid biases from instrumental systematics. In this paper we present three methods for calibrating a global 21-cm cosmology experiment using the noise wave parameter formalisation to characterise a low noise amplifier including a careful consideration of how calibrator temperature noise and singularities will bias the result. The first method presented in this paper builds upon the existing conjugate priors method by weighting the calibrators by a physically motivated factor, thereby avoiding singularities and normalising the noise. The second method fits polynomials to the noise wave parameters by marginalising over the polynomial coefficients and sampling the polynomial orders as parameters. The third method introduces a physically motivated noise model to the marginalised polynomial method. Running these methods on a suite of simulated datasets based on the REACH receiver design and a lab dataset, we found that our methods produced a calibration solution which is equally as or more accurate than the existing conjugate priors method when compared with an analytic estimate of the calibrator's noise. We find in the case of the measured lab dataset the conjugate priors method is biased heavily by the large noise on the shorted load calibrator, resulting in incorrect noise wave parameter fits. This is mitigated by the methods introduced in this paper which calibrate the validation source spectra to within 5% of the noise floor.

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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 %.

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arXiv2023

A Bayesian Method to Mitigate the Effects of Unmodelled Time-Varying Systematics for 21-cm Cosmology Experiments

Radio observations of the neutral hydrogen signal from the Cosmic Dawn and Epoch of Reionisation have helped to provide constraints on the properties of the first stars and galaxies. Since this global 21-cm cosmological signal from the Cosmic Dawn is effectively constant on observing timescales and since effects resulting from systematics will vary with time, the effects of these systematics can be mitigated without the need for a model of the systematic. We present a method to account for unmodelled time-varying systematics in 21-cm radio cosmology experiments using a squared-exponential Gaussian process kernel to account for correlations between time bins in a fully Bayesian way. We find by varying the model parameters of a simulated systematic that the Gaussian process method improves our ability to recover the signal parameters by widening the posterior in the presence of a systematic and reducing the bias in the mean fit parameters. When varying the amplitude of a model sinusoidal systematic between 0.25 and 2.00 times the 21-cm signal amplitude and the period between 0.5 and 4.0 times the signal width, we find on average a 5% improvement in the root mean squared error of the fitted signal. We can use the fitted Gaussian process hyperparameters to identify the presence of a systematic in the data, demonstrating the method's utility as a diagnostic tool. Furthermore, we can use Gaussian process regression to calculate a mean fit to the residuals over time, providing a basis for producing a model of the time-varying systematic.

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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.

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