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Jonathan C. Pober

Publications and source records attributed to Jonathan C. Pober.

At least 19 recordsLinked to original sources

Quantifying Foreground Contamination in the Dark Ages 21 cm Power Spectrum Using an Electromagnetically Simulated Dipole Antenna Atop a Dielectric Surface

The highly-redshifted 21 cm signal from the cosmic Dark Ages presents an exciting frontier for cosmology, with the potential to observe a large number of Fourier modes of the cosmic density field in the absence of complicating astrophysical phenomena. Because the Earth's ionosphere and human-generated interference affect these low radio frequencies, the lunar far side - the most radio-quiet region in the inner solar system - is considered the ideal site to conduct such an experiment. The bright synchrotron foregrounds at these frequencies are expected to be spectrally smooth and thus occupy a subset of spectral Fourier modes, leaving an observable "window" to the Dark Ages signal. However, in practice, spectral structure arises in foregrounds due to instrumental artifacts, leading to a spillover of foreground power into the observable window. In this paper, we quantify this spillover for a set of simulated visibility measurements given a number of antenna beams, sky models, and baseline configurations. Notably, we examine the effects on foreground spillover due to variations in the lunar regoliths using two numerically simulated beam models with 1 and 4 layers of substrate materials underneath. We find that the regolith material properties can induce unwanted spectral structure in the beams, which potentially prevents foreground power suppression at the levels required. The detailed spectral behavior depends on the lunar regolith model used, making it paramount to produce both accurate models of foregrounds and of the lunar regolith for Dark Ages cosmology.

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Mitigating Simulator Dependence in AI Parameter Inference for the Epoch of Reionization: The Importance of Simulation Diversity

The 21cm signal of neutral hydrogen contains a wealth of information about the poorly constrained era of cosmological history, the Epoch of Reionization (EoR). Recently, AI models trained on EoR simulations have gained significant attention as a powerful and flexible option for inferring parameters from 21cm observations. However, previous works show that AI models trained on data from one simulator fail to generalize to data from another, raising doubts about AI models' ability to accurately infer parameters from observation. We develop a new strategy for training AI models on cosmological simulations based on the principle that increasing the diversity of the training dataset improves model robustness by averaging out spurious and contradictory information. We train AI models on data from different combinations of four simulators, then compare the models' performance when predicting on data from held-out simulators acting as proxies for the real universe. We find that models trained on data from multiple simulators perform better on data from a held-out simulator than models trained on data from a single simulator, indicating that increasing the diversity of the training dataset improves a model's ability to generalize. This result suggests that future EoR parameter inference methods can mitigate simulator-specific bias by incorporating multiple simulation approaches into their analyses.

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Towards a Robust Machine-Learning Pipeline for 21-cm Cosmology Data Analysis I: A Roadmap for Development and Demonstration of Robustness Against PSF Modeling Errors

The 21-cm signal from the Epoch of Reionization (EoR) is a powerful probe of the evolution of the Universe. However, accurate measurements of the EoR signal from radio interferometric observations are sensitive to efficient foreground removal, mitigating radio-frequency interference and accounting for instrumental systematics. This work represents the first in a series of papers, where we will be introducing a novel ML based pipeline, step-by-step, to directly infer reionization parameters from 21-cm radio-interferometric images. In this paper, we investigate the impact of the variations in the point spread function (PSF) on parameter estimation by simulating visibilities corresponding to input 21-cm maps as observed by the 128-antenna configuration of the Murchison Widefield Array (MWA) Phase II. These visibilities are imaged to obtain dirty images, which are then used to train a 2D convolutional neural network (CNN) to predict $\rm x_{HI}$. To systematically assess the effect of PSF mis-modelling, we generate multiple test sets by varying the MWA's antenna layout, thereby introducing controlled variations in the PSF; we then feed these alternative PSF dirty images to our CNN trained using only dirty images with the PSF of the true antenna layout. Our results demonstrate that PSF variations introduce biases in the CNN's predictions of $\rm x_{HI}$, with errors depending on the extent of PSF distortion. We quantify these biases and discuss their implications for the reliability of machine-learning-based parameter inference in 21-cm cosmology and how they can be utilized to improve the robustness of estimation against PSF-related systematics in future 21-cm surveys. In concluding, we also discuss how this approach to incorporating realistic instrument error into an ML analysis pipeline can be expanded to include multiple other effects.

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The Impact of Foregrounds on Dark Ages Measurements with the Highly Redshifted 21 cm Line

Studies of the cosmic dark ages ($30 \lesssim z \lesssim 150$) using the highly redshifted 21 cm line of neutral hydrogen offer unparalleled amounts of cosmological information, and recent years have seen the refinement of concepts for such experiments (e.g. CoDEX and FarView), nominally feasible with technology and resources in the next one to two decades. This work studies how the "foreground wedge" -- a term in the 21 cm cosmology literature referring to the contamination of power spectrum modes through the combination of smooth-spectrum foreground emission and the frequency-dependent point spread function of a radio interferometer -- manifests at these very high redshifts. We find the effect is more significant than at Epoch of Reionization redshifts targeted by current ground-based experiments, with foreground avoidance techniques (which discard all $k$ modes falling within the wedge) typically losing an order of magnitude of sensitivity. Given the extreme faintness of the 21 cm signal from the cosmic dark ages and the very high sky temperatures (the dominant source of noise) at low radio frequencies, we conclude that some level of foreground subtraction will be necessary to enable dark ages 21 cm cosmology with experiments of the scale believed to be achievable in the near term.

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Detecting the 21 cm Signal of the Cosmic Dark Ages

The cosmic "Dark Ages" is the period between the last scattering of the Cosmic Microwave Background (CMB) and the appearance of the first luminous sources, spanning redshifts $1100\gtrsim z\gtrsim 30$. The only way to observe this period is by examining the 21 cm hyperfine transition line of neutral hydrogen HI, which -- given the high redshifts (and hence long wavelengths) -- must be observed from outside the Earth's ionosphere. Given the faintness of the signal, concepts for a radio array on the lunar far side (where large collecting areas can be deployed and radio frequency interference is minimal) have been proposed, like FarView or FARSIDE, but designs are still in the preliminary stages. This paper studies multiple aspects of array design to determine the impact of different design decisions on sensitivity to the Dark Ages 21 cm power spectrum. We do so by using the sensitivity package 21cmSense to model and simulate various array configurations. We present a fiducial design based on a modification of the FarView concept, which consists of a collecting area of $\sim2.5\,\rm{km}^{2}$ with 82,944 tightly packed dual-polarization dipoles grouped into 5,184 correlated elements, or subarrays, delivering a $>10\sigma$ detection of the $z=30$ signal with a five year lifetime. We find that, beyond mere collecting area, the most important factor in achieving this sensitivity is the presence of very short baselines that can only be realized with small, closely packed antennas.

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Altitude Estimation of Radio Frequency Interference Sources via Interferometric Near Field Corrections

Radio-frequency interference (RFI) presents a significant obstacle to current radio interferometry experiments aimed at the Epoch of Reionization. RFI contamination is often several orders of magnitude brighter than the astrophysical signals of interest, necessitating highly precise identification and flagging. Although existing RFI flagging tools have achieved some success, the pervasive nature of this contamination leads to the rejection of excessive data volumes. In this work, we present a way to estimate an RFI emitter's altitude using near-field corrections. Being able to obtain the precise location of such an emitter could shift the strategy from merely flagging to subtracting or peeling the RFI, allowing us to preserve a higher fraction of usable data. We conduct a preliminary study using a two-minute observation from the Murchison-Widefield Array (MWA) in which an unknown object briefly crosses the field of view, reflecting RFI signals into the array. By applying near-field corrections that bring the object into focus, we are able to estimate its approximate altitude and speed to be $11.7$ km and $792$ km/h, respectively. This allows us to confidently conclude that the object in question is in fact an airplane. We further validate our technique through the analysis of two additional RFI-containing MWA observations, where we are consistently able to identify airplanes as the source of the interference.

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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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$χ^2$ from Redundant Calibration as a Tool in the Detection of Faint Radio-frequency Interference

Radio-frequency interference detection and flagging is one of the most difficult and urgent problems in 21 cm Epoch of Reionization research. In this work, we present $χ^2$ from redundant calibration as a novel method for RFI detection and flagging, demonstrating it to be complementary to current state-of-the-art flagging algorithms. Beginning with a brief overview of redundant calibration and the meaning of the $χ^2$ metric, we demonstrate a two-step RFI flagging algorithm which uses the values of this metric to detect faint RFI. We find that roughly 27.4\% of observations have RFI from digital television channel 7 detected by at least one algorithm of the three tested: 18.0\% of observations are flagged by the novel $χ^2$ algorithm, 16.5\% are flagged by SSINS, and 6.8\% are flagged by AOFlagger (there is significant overlap in these percentages). Of the 27.4\% of observations with detected DTV channel 7 RFI, 37.1\% (10.2\% of the total observations) are detected by $χ^2$ alone, and not by either SSINS or AOFlagger, demonstrating a significant population of as-yet undetected RFI. We find that $χ^2$ is able to detect RFI events which remain undetectable to SSINS and AOFlagger, especially in the domain of long-duration, weak RFI from digital television. We also discuss the shortcomings of this approach, and discuss examples of RFI which seems undetectable using $χ^2$ while being successfully flagged by SSINS and/or AOFlagger.

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Evidence of Ultra-faint Radio Frequency Interference in Deep 21~cm Epoch of Reionization Power Spectra with the Murchison Widefield Array

We present deep upper limits from the 2014 Murchison Widefield Array (MWA) Phase I observing season, with a particular emphasis on identifying the spectral fingerprints of extremely faint radio frequency interference (RFI) contamination in the 21~cm power spectra (PS). After meticulous RFI excision involving a combination of the \textsc{SSINS} RFI flagger and a series of PS-based jackknife tests, our lowest upper limit on the Epoch of Reionization (EoR) 21~cm PS signal is $\Delta^2 \leq 1.61\cdot10^4 \text{ mK}^2$ at $k=0.258\text{ h Mpc}^{-1}$ at a redshift of 6.5 using 14.7 hours of data. By leveraging our understanding of how even fainter RFI is likely to contaminate the EoR PS, we are able to identify ultra-faint RFI signals in the cylindrical PS. Surprisingly this signature is most obvious in PS formed with less than an hour of data, but is potentially subdominant to other systematics in multiple-hour integrations. Since the total RFI budget in a PS detection is quite strict, this nontrivial integration behavior suggests a need to more realistically model coherently integrated ultra-faint RFI in PS measurements so that its potential contribution to a future detection can be diagnosed.

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Search for the Epoch of Reionisation with HERA: Upper Limits on the Closure Phase Delay Power Spectrum

Radio interferometers aiming to measure the power spectrum of the redshifted 21 cm line during the Epoch of Reionisation (EoR) need to achieve an unprecedented dynamic range to separate the weak signal from overwhelming foreground emissions. Calibration inaccuracies can compromise the sensitivity of these measurements to the effect that a detection of the EoR is precluded. An alternative to standard analysis techniques makes use of the closure phase, which allows one to bypass antenna-based direction-independent calibration. Similarly to standard approaches, we use a delay spectrum technique to search for the EoR signal. Using 94 nights of data observed with Phase I of the Hydrogen Epoch of Reionization Array (HERA), we place approximate constraints on the 21 cm power spectrum at $z=7.7$. We find at 95% confidence that the 21 cm EoR brightness temperature is $\le$(372)$^2$ "pseudo" mK$^2$ at 1.14 "pseudo" $h$ Mpc$^{-1}$, where the "pseudo" emphasises that these limits are to be interpreted as approximations to the actual distance scales and brightness temperatures. Using a fiducial EoR model, we demonstrate the feasibility of detecting the EoR with the full array. Compared to standard methods, the closure phase processing is relatively simple, thereby providing an important independent check on results derived using visibility intensities, or related.

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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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Impact of instrument and data characteristics in the interferometric reconstruction of the 21 cm power spectrum

Combining the visibilities measured by an interferometer to form a cosmological power spectrum is a complicated process. In a delay-based analysis, the mapping between instrumental and cosmological space is not a one-to-one relation. Instead, neighbouring modes contribute to the power measured at one point, with their respective contributions encoded in the window functions. To better understand the power measured by an interferometer, we assess the impact of instrument characteristics and analysis choices on these window functions. Focusing on the Hydrogen Epoch of Reionization Array (HERA) as a case study, we find that long-baseline observations correspond to enhanced low-k tails of the window functions, which facilitate foreground leakage, whilst an informed choice of bandwidth and frequency taper can reduce said tails. With simple test cases and realistic simulations, we show that, apart from tracing mode mixing, the window functions help accurately reconstruct the power spectrum estimator of simulated visibilities. The window functions depend strongly on the beam chromaticity, and less on its spatial structure - a Gaussian approximation, ignoring side lobes, is sufficient. Finally, we investigate the potential of asymmetric window functions, down-weighting the contribution of low-k power to avoid foreground leakage. The window functions presented here correspond to the latest HERA upper limits for the full Phase I data. They allow an accurate reconstruction of the power spectrum measured by the instrument and will be used in future analyses to confront theoretical models and data directly in cylindrical space.

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Direct Optimal Mapping for 21cm Cosmology: A Demonstration with the Hydrogen Epoch of Reionization Array

Motivated by the desire for wide-field images with well-defined statistical properties for 21cm cosmology, we implement an optimal mapping pipeline that computes a maximum likelihood estimator for the sky using the interferometric measurement equation. We demonstrate this direct optimal mapping with data from the Hydrogen Epoch of Reionization (HERA) Phase I observations. After validating the pipeline with simulated data, we develop a maximum likelihood figure-of-merit for comparing four sky models at 166MHz with a bandwidth of 100kHz. The HERA data agree with the GLEAM catalogs to <10%. After subtracting the GLEAM point sources, the HERA data discriminate between the different continuum sky models, providing most support for the model of Byrne et al. 2021. We report the computation cost for mapping the HERA Phase I data and project the computation for the HERA 320-antenna data; both are feasible with a modern server. The algorithm is broadly applicable to other interferometers and is valid for wide-field and non-coplanar arrays.

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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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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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The Optical Depth of Foregrounds for the Highest Redshift 21 cm Signals

Foreground emission makes it difficult to detect the highly-redshifted cosmological 21 cm signal at any frequency. However, at low frequencies foregrounds are likely to become optically thick, which would make it completely impossible to see a 21 cm signal behind them. To find out which regions of the sky might be optically thick for the highest redshifts of the 21 cm signal, we fit the measurements from LWA1 and the Haslam 408 MHz map with a two-component spectral model and calculate the frequency-dependent foreground optical depth point-by-point across the sky. Limitations of the current data prevent us from making any strong conclusions at high statistical significance, but there is suggestive evidence ($\sim1σ$) that as much as 25% of the sky could be obscured for the highest redshift 21 cm signals.

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Automated Detection of Antenna Malfunctions in Large-N Interferometers: A Case Study with the Hydrogen Epoch of Reionization Array

We present a framework for identifying and flagging malfunctioning antennas in large radio interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study. Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets.

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The Correlation Calibration of PAPER-64 data

Observation of redshifted 21-cm signal from the Epoch of Reionization (EoR) is challenging due to contamination from the bright foreground sources that exceed the signal by several orders of magnitude. The removal of this very high foreground relies on accurate calibration to keep the intrinsic property of the foreground with frequency. Commonly employed calibration techniques for these experiments are the sky model-based and the redundant baseline-based calibration approaches. However, the sky model-based and redundant baseline-based calibration methods could suffer from sky-modeling error and array redundancy imperfection issues, respectively. In this work, we introduce the hybrid correlation calibration ("CorrCal") scheme, which aims to bridge the gap between redundant and sky-based calibration by relaxing redundancy of the array and including sky information into the calibration formalisms. We demonstrate the slight improvement of power spectra, about $-6\%$ deviation at the bin right on the horizon limit of the foreground wedge-like structure, relative to the power spectra before the implementation of "CorrCal" to the data from the Precision Array for Probing the Epoch of Reionization (PAPER) experiment, which was otherwise calibrated using redundant baseline calibration. This small improvement of the foreground power spectra around the wedge limit could be suggestive of reduced spectral structure in the data after "CorrCal" calibration, which lays the foundation for future improvement of the calibration algorithm and implementation method.

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