arXiv subjects

Peter H. Sims

Publications and source records attributed to Peter H. Sims.

At least 19 recordsLinked to original sources

arXiv2026

Foreground Characterization and Mitigation in the Observations of the CD/EoR with the SKA

The Square Kilometre Array (SKA), with its unprecedented sensitivity, frequency coverage, and large collecting area, is poised to revolutionize our understanding of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) epochs marking the formation of the first luminous sources and the subsequent reionization of the intergalactic medium (IGM). However, detecting the faint redshifted 21-cm signal from neutral hydrogen remains one of the foremost challenges in observational cosmology, as it is buried beneath bright foregrounds from Galactic synchrotron radiation, free-free emission, and extragalactic point sources that are 4-5 orders of magnitude stronger than the cosmological signal. In this chapter, we highlight the key components and characteristics of these foregrounds and review ongoing efforts to model, characterize, and mitigate them. We emphasize how the SKA-Low AA* configuration, through its optimized array design, wide field of view, and improved calibration accuracy, enhances our capacity to suppress foreground contamination and recover the cosmological signal. The SKA Observatory Foreground Challenge plays a pivotal role in this effort by bringing together the global EoR/CD community to develop, compare, and validate foreground removal pipelines using realistic simulated datasets. Building on the experience of existing pathfinders such as LOFAR, MWA, and HERA, these collaborative initiatives are helping refine statistical and machine learning-based approaches for signal recovery. Together, these advancements are laying the groundwork for the SKA to probe the thermal and ionization history of the early Universe with unprecedented precision.

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arXiv2026

Overview of 21cm Experiments at high redshift with SKAO

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low experiments and expected sensitivity - power spectrum, tomography, 21-cm forest, cross-correlations, building on the broad observational plan laid out in the 2015 SKA Science Book. Finally, we outline features of the telescope that will be critical for the success of EoR/CD science, e.g., beam apodization, substations, and multi-beaming.

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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.

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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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arXiv2025

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- II. Model priors and posterior odds

The reliable detection of the global 21-cm signal, a key tracer of Cosmic Dawn and the Epoch of Reionization, requires meticulous data modelling and robust statistical frameworks for model validation and comparison. In Paper I of this series, we presented the Beam-Factor-based Chromaticity Correction (BFCC) model for spectrometer data processed using BFCC to suppress instrumentally induced spectral structure. We demonstrated that the BFCC model, with complexity calibrated by Bayes factor-based model comparison (BFBMC), enables unbiased recovery of a 21-cm signal consistent with the one reported by EDGES from simulated data. Here, we extend the evaluation of the BFCC model to lower amplitude 21-cm signal scenarios where deriving reliable conclusions about a model's capacity to recover unbiased 21-cm signal estimates using BFBMC is more challenging. Using realistic simulations of chromaticity-corrected EDGES-low spectrometer data, we evaluate three signal amplitude regimes -- null, moderate, and high. We then conduct a Bayesian comparison between the BFCC model and three alternative models previously applied to 21-cm signal estimation from EDGES data. To mitigate biases introduced by systematics in the 21-cm signal model fit, we incorporate the Bayesian Null-Test-Evidence-Ratio (BaNTER) validation framework and implement a Bayesian inference workflow based on posterior odds of the validated models. We demonstrate that, unlike BFBMC alone, this approach consistently recovers 21-cm signal estimates that align with the true signal across all amplitude regimes, advancing robust global 21-cm signal detection methodologies.

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arXiv2025

Rapid and Late Cosmic Reionization Driven by Massive Galaxies: a Joint Analysis of Constraints from 21-cm, Lyman Line & CMB Data Sets

Observations of the Epoch of Reionization (EoR) have the potential to answer long-standing questions of astrophysical interest regarding the nature of the first luminous sources and their effects on the intergalactic medium (IGM). We present astrophysical constraints from a Neural Density Estimation-Accelerated Bayesian joint analysis of constraints deriving from Cosmic Microwave Background power spectrum measurements from Planck and SPT, IGM neutral fraction measurements from Lyman-line-based data sets and 21-cm power spectrum upper limits from HERA, LOFAR and the MWA. In the context of the model employed, the data is found to be consistent with galaxies forming from predominantly atomic-cooled hydrogen gas in dark matter halos, with masses $M_\mathrm{min} \gtrsim 2.6 \times 10^{9}~M_{\odot} ((1+z)/10)^{\frac{1}{2}}$ at 95% credibility ($V_\mathrm{c} \gtrsim 50~\mathrm{km~s^{-1}}$) being the dominant galactic population driving reionization. These galaxies reionize the neutral hydrogen in the IGM over a narrow redshift interval ($\Delta z_\mathrm{re} < 1.8$ at 95% credibility), with the midpoint of reionization (when the sky-averaged IGM neutral fraction is 50%) constrained to $z_{50} = 7.16^{+0.15}_{-0.12}$. Given the parameter posteriors from our joint analysis, we find that the posterior predictive distribution of the global 21-cm signal is reduced in amplitude and shifted to lower redshifts relative to the model prior. We caution, however, that our inferences are model-dependent. Future work incorporating updated, mass-dependent star formation efficiencies in atomic cooling halos, informed by the latest UV luminosity function constraints from the James Webb Space Telescope, promises to refine these inferences further and enhance our understanding of cosmic reionization.

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arXiv2025

A general Bayesian model-validation framework based on null-test evidence ratios, with an example application to global 21-cm cosmology

Comparing composite models for multi-component observational data is a prevalent scientific challenge. When fitting composite models, there exists the potential for systematics from a poor fit of one model component to be absorbed by another, resulting in the composite model providing an accurate fit to the data in aggregate but yielding biased a posteriori estimates for individual components. We begin by defining a classification scheme for composite model comparison scenarios, identifying two categories: category I, where models with accurate and predictive components are separable through Bayesian comparison of the unvalidated composite models, and category II, where models with accurate and predictive components may not be separable due to interactions between components, leading to spurious detections or biased signal estimation. To address the limitations of category II model comparisons, we introduce the Bayesian Null Test Evidence Ratio-based (BaNTER) validation framework. Applying this classification scheme and BaNTER to a composite model comparison problem in 21-cm cosmology, where minor systematics from imperfect foreground modelling can bias global 21-cm signal recovery, we validate six composite models using mock data. We show that incorporating BaNTER alongside Bayes-factor-based comparison reliably ensures unbiased inferences of the signal of interest across both categories, positioning BaNTER as a valuable addition to Bayesian inference workflows with potential applications across diverse fields.

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arXiv2025

BayesEoR: Bayesian 21-cm Power Spectrum Estimation from Interferometric Visibilities

BayesEoR is a GPU-accelerated, MPI-compatible Python package for estimating the power spectrum of redshifted 21-cm emission from interferometric observations of the Epoch of Reionization (EoR). Utilizing a Bayesian framework, BayesEoR jointly fits for the 21-cm EoR power spectrum and a "foreground" model, referring to bright, contaminating emission between us and the cosmological signal, and forward models the instrument with which these signals are observed. To perform the sampling, we use MultiNest [arXiv:1402.0004], which calculates the Bayesian evidence as part of the analysis. Thus, BayesEoR can also be used as a tool for model selection [see e.g. arXiv:1701.03384].

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arXiv2023

All Sky Modelling Requirements for Bayesian 21 cm Power Spectrum Estimation with BayesEoR

We present a comprehensive simulation-based study of the BayesEoR code for 21 cm power spectrum recovery when analytically marginalizing over foreground parameters. To account for covariance between the 21 cm signal and contaminating foreground emission, BayesEoR jointly constructs models for both signals within a Bayesian framework. Due to computational constraints, the forward model is constructed using a restricted field-of-view (FoV) in the image domain. When the only EoR contaminants are noise and foregrounds, we demonstrate that BayesEoR can accurately recover the 21 cm power spectrum when the component of sky emission outside this forward-modelled region is downweighted by the beam at the level of the dynamic range between the foreground and 21 cm signals. However, when all-sky foreground emission is included along with a realistic instrument primary beam with sidelobes above this threshold extending to the horizon, the recovered power spectrum is contaminated by unmodelled sky emission outside the restricted FoV model. Expanding the combined cosmological and foreground model to cover the whole sky is computationally prohibitive. To address this, we present a modified version of BayesEoR that allows for an all-sky foreground model, while the modelled 21 cm signal remains only within the primary FoV of the telescope. With this modification, it will be feasible to run an all-sky BayesEoR analysis on a sizeable compute cluster. We also discuss several future directions for further reducing the need to model all-sky foregrounds, including wide-field foreground subtraction, an image-domain likelihood utilizing a tapering function, and instrument primary beam design.

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arXiv2022

Analytic approximations of scattering effects on beam chromaticity in 21-cm global experiments

Scattering from objects near an antenna produce correlated signals from strong compact radio sources in a manner similar to those used by the Sea Interferometer to measure the radio source positions using the fine frequency structure in the total power spectrum of a single antenna. These fringes or ripples due to correlated signal interference are present at a low level in the spectrum of any single antenna and are a major source of systematics in systems used to measure the global redshifted 21-cm signal from the early universe. In the Sea Interferometer a single antenna on a cliff above the sea is used to add the signal from the direct path to the signal from the path reflected from the sea thereby forming an interferometer. This was used for mapping radio sources with a single antenna by Bolton and Slee in the 1950s. In this paper we derive analytic expressions to determine the level of these ripples and compare these results in a few simple cases with electromagnetic modeling software to verify that the analytic calculations are sufficient to obtain the magnitude of the scattering effects on the measurements of the global 21-cm signal. These analytic calculations are needed to evaluate the magnitude of the effects in cases that are either too complex or take too much time to be modeled using software.

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arXiv2022

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- I. Mathematical formalism

Accurately accounting for spectral structure in spectrometer data induced by instrumental chromaticity on scales relevant for detection of the 21-cm signal is among the most significant challenges in global 21-cm signal analysis. In the publicly available EDGES low-band data set, this complicating structure is suppressed using beam-factor based chromaticity correction (BFCC), which works by dividing the data by a sky-map-weighted model of the spectral structure of the instrument beam. Several analyses of this data have employed models that start with the assumption that this correction is complete. However, while BFCC mitigates the impact of instrumental chromaticity on the data, given realistic assumptions regarding the spectral structure of the foregrounds, the correction is only partial. This complicates the interpretation of fits to the data with intrinsic sky models (models that assume no instrumental contribution to the spectral structure of the data). In this paper, we derive a BFCC data model from an analytic treatment of BFCC and demonstrate using simulated observations that, in contrast to using an intrinsic sky model for the data, the BFCC data model enables unbiased recovery of a simulated global 21-cm signal from beam-factor chromaticity corrected data in the limit that the data is corrected with an error-free beam-factor model.

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arXiv2022

A Bayesian Calibration Framework for EDGES

We develop a Bayesian model that jointly constrains receiver calibration, foregrounds and cosmic 21cm signal for the EDGES global 21\,cm experiment. This model simultaneously describes calibration data taken in the lab along with sky-data taken with the EDGES low-band antenna. We apply our model to the same data (both sky and calibration) used to report evidence for the first star formation in 2018. We find that receiver calibration does not contribute a significant uncertainty to the inferred cosmic signal (<1%), though our joint model is able to more robustly estimate the cosmic signal for foreground models that are otherwise too inflexible to describe the sky data. We identify the presence of a significant systematic in the calibration data, which is largely avoided in our analysis, but must be examined more closely in future work. Our likelihood provides a foundation for future analyses in which other instrumental systematics, such as beam corrections and reflection parameters, may be added in a modular manner.

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arXiv2022

A Bayesian approach to high fidelity interferometric calibration II: demonstration with simulated data

In a companion paper, we presented BayesCal, a mathematical formalism for mitigating sky-model incompleteness in interferometric calibration. In this paper, we demonstrate the use of BayesCal to calibrate the degenerate gain parameters of full-Stokes simulated observations with a HERA-like hexagonal close-packed redundant array, for three assumed levels of completeness of the a priori known component of the calibration sky model. We compare the BayesCal calibration solutions to those recovered by calibrating the degenerate gain parameters with only the a priori known component of the calibration sky model both with and without imposing physically motivated priors on the gain amplitude solutions and for two choices of baseline length range over which to calibrate. We find that BayesCal provides calibration solutions with up to four orders of magnitude lower power in spurious gain amplitude fluctuations than the calibration solutions derived for the same data set with the alternate approaches, and between $\sim10^7$ and $\sim10^{10}$ times smaller than in the mean degenerate gain amplitude on the full range of spectral scales accessible in the data. Additionally, we find that in the scenarios modelled only BayesCal has sufficiently high fidelity calibration solutions for unbiased recovery of the 21 cm power spectrum on large spectral scales ($k_\parallel \lesssim 0.15~h\mathrm{Mpc}^{-1}$). In all other cases, in the completeness regimes studied, those scales are contaminated.

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arXiv2022

A Bayesian approach to high fidelity interferometric calibration I: mathematical formalism

High fidelity radio interferometric data calibration that minimises spurious spectral structure in the calibrated data is essential in astrophysical applications, such as 21 cm cosmology, which rely on knowledge of the relative spectral smoothness of distinct astrophysical emission components to extract the signal of interest. Existing approaches to radio interferometric calibration have been shown to impart spurious spectral structure to the calibrated data if the sky model used to calibrate the data is incomplete. In this paper, we introduce BayesCal: a novel solution to the sky-model incompleteness problem in interferometric calibration, designed to enable high fidelity data calibration. The BayesCal data model supplements the a priori known component of the forward model of the sky with a statistical model for the missing and uncertain flux contribution to the data, constrained by a prior on the power in the model. We demonstrate how the parameters of this model can be marginalised out analytically, reducing the dimensionality of the parameter space to be sampled from and allowing one to sample directly from the posterior probability distribution of the calibration parameters. Additionally, we show how physically motivated priors derived from theoretical and measurement-based constraints on the spectral smoothness of the instrumental gains can be used to constrain the calibration solutions. In a companion paper, we apply this algorithm to simulated observations with a HERA-like array and demonstrate that it enables up to four orders of magnitude suppression of power in spurious spectral fluctuations relative to standard calibration approaches.

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arXiv2022

Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators

Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic Dawn among other phenomena. The method provides a way to fit complex models to data describing key cosmological and astrophysical signals and a whole host of contaminating signals and instrumental effects modelled with `nuisance parameters'. In this paper, we summarise a method that uses Masked Autoregressive Flows and Kernel Density Estimators to learn marginal posterior densities corresponding to core science parameters. We find that the marginal or 'nuisance-free' posteriors and the associated likelihoods have an abundance of applications including; the calculation of previously intractable marginal Kullback-Leibler divergences and marginal Bayesian Model Dimensionalities, likelihood emulation and prior emulation. We demonstrate each application using toy examples, examples from the field of 21-cm cosmology and samples from the Dark Energy Survey. We discuss how marginal summary statistics like the Kullback-Leibler divergences and Bayesian Model Dimensionalities can be used to examine the constraining power of different experiments and how we can perform efficient joint analysis by taking advantage of marginal prior and likelihood emulators. We package our multipurpose code up in the pip-installable code margarine for use in the wider scientific community.

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arXiv2019

Testing for calibration systematics in the EDGES low-band data using Bayesian model selection

Cosmic Dawn, when the first stars and proto-galaxies began to form, is commonly expected to be accompanied by an absorption signature at radio frequencies. This feature arises as Lyman-$α$ photons emitted by these first luminous objects couple the 21 cm excitation temperature of intergalactic hydrogen gas to its kinetic temperature, driving it into absorption relative to the CMB. The detailed properties of this absorption profile encode powerful information about the physics of Cosmic Dawn. Recently, Bowman et al. analysed data from the EDGES low-band radio antenna and found an unexpectedly deep absorption profile centred at 78 MHz, which could be a detection of this signature. Their specific analysis fit their measurements using a polynomial foreground model, a flattened Gaussian absorption profile and a white noise model; we argue that a more accurate model, that includes a detailed noise model and accounting for the effects of plausible calibration errors, is essential for describing the EDGES data set. We perform a Bayesian evidence-based comparison of models of the EDGES low-band data set and find that those incorporating these additional components are decisively preferred. The subset of the best fitting models of the data that include a global signal favour an amplitude consistent with standard cosmological assumptions (A < 209 mK). However, there is not strong evidence to favour models of the data including a global 21 cm signal over those without one. Ultimately, we find that the derivation of robust constraints on astrophysics from the data is limited by the presence of systematics.

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arXiv2019

A Roadmap for Astrophysics and Cosmology with High-Redshift 21 cm Intensity Mapping

In this white paper, we lay out a US roadmap for high-redshift 21 cm cosmology (30 < z < 6) in the 2020s. Beginning with the currently-funded HERA and MWA Phase II projects and advancing through the decade with a coordinated program of small-scale instrumentation, software, and analysis projects targeting technology development, this roadmap incorporates our current best understanding of the systematics confronting 21 cm cosmology into a plan for overcoming them, enabling next-generation, mid-scale 21 cm arrays to be proposed late in the decade. Submitted for consideration by the Astro2020 Decadal Survey Program Panel for Radio, Millimeter, and Submillimeter Observations from the Ground as a Medium-Sized Project.

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arXiv2019

Joint estimation of the Epoch of Reionization power spectrum and foregrounds

The power spectrum of redshifted 21 cm emission brightness temperature fluctuations is a powerful probe of the Epoch of Reionization (EoR). However, bright foreground emission presents a significant impediment to its unbiased recovery from interferometric data. We build on the Bayesian power spectral estimation methodology introduced in Sims et al. 2016 and demonstrate that incorporating a priori knowledge of the spectral structure of foregrounds in the large spectral scale component of the data model enables significantly improved modelling of the foregrounds without increasing the model complexity. We explore two astrophysically motivated parametrisations of the large spectral scale model: (i) a constant plus power law model of the form $q_{0}+q_{1}(ν/ν_{0})^{b_{1}}$ for two values of $b_{1}$: $b_{1} = <β>_\mathrm{GDSE}$ and $b_{1} = <β>_\mathrm{EGS}$, the mean spectral indices of the Galactic diffuse synchrotron emission and extragalactic source foreground emission, respectively, and (ii) a constant plus double power law model of the form $q_{0}+q_{1}(ν/ν_{0})^{b_{1}}+q_{2}(ν/ν_{0})^{b_{2}}$ with $b_{1} = <β>_\mathrm{GDSE}$ and $b_{2} = <β>_\mathrm{EGS}$. We estimate the EoR power spectrum from simulated interferometric data consisting of an EoR signal, Galactic diffuse synchrotron emission, extragalactic sources and diffuse free-free emission from the Galaxy. We show that, by jointly estimating a model of the EoR signal with the constant plus double power law parametrisation of the large spectral scale model, unbiased estimates of the EoR power spectrum are recoverable on all spatial scales accessible in the data set, including on the large spatial scales that were found to be contaminated in earlier work.

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