Characterizing Signal Loss in the 21 cm Reionization Power Spectrum: A Revised Study of PAPER-64

The Epoch of Reionization (EoR) is an uncharted era in our Universe's history during which the birth of the first stars and galaxies led to the ionization of neutral hydrogen in the intergalactic medium. There are many experiments investigating the EoR by tracing the 21cm line of neutral hydrogen. Because this signal is very faint and difficult to isolate, it is crucial to develop analysis techniques that maximize sensitivity and suppress contaminants in data. It is also imperative to understand the trade-offs between different analysis methods and their effects on power spectrum estimates. Specifically, with a statistical power spectrum detection in HERA's foreseeable future, it has become increasingly important to understand how certain analysis choices can lead to the loss of the EoR signal. In this paper, we focus on signal loss associated with power spectrum estimation. We describe the origin of this loss using both toy models and data taken by the 64-element configuration of the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER). In particular, we highlight how detailed investigations of signal loss have led to a revised, higher 21cm power spectrum upper limit from PAPER-64. Additionally, we summarize errors associated with power spectrum error estimation that were previously unaccounted for. We focus on a subset of PAPER-64 data in this paper; revised power spectrum limits from the PAPER experiment are presented in a forthcoming paper by Kolopanis et al. (in prep.) and supersede results from previously published PAPER analyses.Comment: 25 pages, 18 figures, Accepted by Ap

Low-Frequency Radio Recombination Lines Away From the Inner Galactic Plane

Diffuse radio recombination lines (RRLs) in the Galaxy are possible foregrounds for redshifted 21~cm experiments. We use EDGES drift scans centered at $-26.7^o$~declination to characterize diffuse RRLs across the southern sky. We find RRLs averaged over the large antenna beam ($72^o \times 110^o$) reach minimum amplitudes between right ascensions~2-6~h. In this region, the C$\alpha$ absorption amplitude is $33\pm11$~mK (1$\sigma$) averaged over 50-87~MHz ($27\gtrsim z \gtrsim15$ for the 21~cm line) and increases strongly as frequency decreases. C$\beta$ and H$\alpha$ lines are consistent with no detection with amplitudes of $13\pm14$ and $12\pm10$~mK (1$\sigma$), respectively. At 108-124.5~MHz ($z\approx11$) in the same region, we find no evidence for carbon or hydrogen lines at the noise level of 3.4~mK (1$\sigma$). Conservatively assuming observed lines come broadly from the diffuse interstellar medium, as opposed to a few compact regions, these amplitudes provide upper limits on the intrinsic diffuse lines. The observations support expectations that Galactic RRLs can be neglected as significant foregrounds for a large region of sky until redshifted 21~cm experiments, particularly those targeting Cosmic Dawn, move beyond the detection phase. We fit models of the spectral dependence of the lines averaged over the large beam of EDGES, which may contain multiple line sources with possible line blending, and find that including degrees of freedom for expected smooth, frequency-dependent deviations from local thermodynamic equilibrium (LTE) is preferred over simple LTE assumptions for C$\alpha$ and H$\alpha$ lines. For C$\alpha$ we estimate departure coefficients $0.79<b_n\beta_n<4.5$ along the inner Galactic Plane and $0<b_n\beta_n<2.3$ away from the inner Galactic Plane.Comment: 25 pages, 14 figures, 3 tables, submitted to AA

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.Comment: 10 pages (plus a cover page and references), 6 figures. Submitted as a APC White Paper for Astro202

A simplified, lossless re-analysis of PAPER-64

We present limits on the 21cm power spectrum from the Epoch of Reionization (EoR) using data from the 64 antenna configuration of the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER) analyzed through a power spectrum pipeline independent from previous PAPER analyses. Previously reported results from PAPER have been found to contain significant signal loss (Cheng et al. 2018, arxiv:1810.05175). Several lossy steps from previous PAPER pipelines have not been included in this analysis, namely: delay-based foreground filtering, optimal fringe-rate filtering, and empirical covariance-based estimators. Steps which remain in common with previous analyses include redundant calibration and local sidereal time (LST) binning. The power spectra reported here are effectively the result of applying a linear Fourier transform analysis to the calibrated, LST binned data. This analysis also uses more data than previous publications, including the complete available redshift range of $z \sim 7.5$ to $11$. In previous PAPER analyses, many power spectrum measurements were found to be detections of noncosmological power at levels of significance ranging from two to hundreds of times the theoretical noise. Here, excess power is examined using redundancy between baselines and power spectrum jackknives. The upper limits we find on the 21cm power spectrum from reionization are ($1500$ mK)$^{2}$, ($1900$ mK)$^{2}$, ($280$ mK)$^{2}$, ($200$ mK)$^{2}$, ($380$ mK)$^{2}$, ($300$ mK)$^{2}$ at redshifts $z=10.87,\ 9.93,\ 8.68,\ 8.37,\ 8.13,$ and $7.48$, respectively. For reasons described in Cheng et al. 2018 (arxiv:1810.05175), these limits supersede all previous PAPER results (Ali et al. 2018, arxiv:1502.06016).Comment: 28 Pages, 17 Pages, Accepted to AP

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

Improved 21 cm Epoch of Reionization Power Spectrum Measurements with a Hybrid Foreground Subtraction and Avoidance Technique

Observations of the 21 cm Epoch of Reionization signal are dominated by Galactic and extragalactic foregrounds. The need for foreground removal has led to the development of two main techniques, often referred to as “foreground avoidance” and “foreground subtraction.” Avoidance is associated with filtering foregrounds in Fourier space, while subtraction uses an explicit foreground model that is removed. Using 1088 hr of data from the 64-element PAPER array, we demonstrate that subtraction of a foreground model prior to delay-space foreground filtering results in a modest but measurable improvement of the performance of the filter. This proof-of-concept result shows that improvement stems from the reduced dynamic range requirements needed for the foreground filter: subtraction of a foreground model reduces the total foreground power, so for a fixed dynamic range, the filter can push toward fainter limits. We also find that the choice of window function used in the foreground filter can have an appreciable affect on the performance near the edges of the observing band. We demonstrate these effects using a smaller 3 hr sampling of data from the MWA, and find that the hybrid filtering and subtraction removal approach provides similar improvements across the band as seen in the case with PAPER-64

Optimizing Sparse RFI Prediction using Deep Learning

Radio Frequency Interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array grow larger in number of receivers. To address this, we present a Deep Fully Convolutional Neural Network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known "ground truth" dataset for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6$\times 10^{5}$ HERA time-ordered 1024 channeled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and Non-RFI. The inclusion of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and $F_{2}$ score of 0.75 as applied to our HERA-67 observations.Comment: 11 pages, 7 figure