Pith. sign in

REVIEW 3 major objections 6 minor 5 cited by

A simplified, lossless re-analysis of PAPER-64

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A simplified, lossless re-analysis turns PAPER-64 detections into upper limits.

desk verdict A useful corrective reanalysis whose qualitative conclusion is credible, but the abstract's upper limits are built from a biased order statistic and need re-derivation before being quoted. read the letter →

arxiv 1909.02085 v1 pith:LZR7SXA4 submitted 2019-09-04 astro-ph.CO

Matthew Kolopanis , Daniel C. Jacobs , Carina Cheng , Aaron R. Parsons , Saul A. Kohn , Jonathan C. Pober , James E. Aguirre , Zaki S. Ali
show 15 more authors
Gianni Bernardi Richard F. Bradley Christopher L. Carilli David R. DeBoer Matthew Dexter Joshua S. Dillon Joshua Kerrigan Patricia Klima Adrian Liu Dave MacMahon David F. Moore Nithyanandan Thyagarajan Chuneeta Devi Nunhokee William Walbrughp Andre Walker
This is my paper · ORCID
classification astro-ph.CO
keywords 21cmcosmologyEpochofReionizationdelaypowerspectrumPAPER-64redundantbaselinessignallossupperlimitsforegroundcontamination
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper re-analyzes archival PAPER-64 data with a deliberately simple pipeline that removes the lossy steps of earlier analyses---delay-based foreground filtering, optimal fringe-rate filtering, and empirical covariance weighting---leaving a uniformly weighted Fourier transform of calibrated, LST-binned visibilities. It finds no significant 21 cm Epoch of Reionization signal, reporting upper limits of $(1500\ \mathrm{mK})^2$, $(1900\ \mathrm{mK})^2$, $(280\ \mathrm{mK})^2$, $(200\ \mathrm{mK})^2$, $(380\ \mathrm{mK})^2$, and $(300\ \mathrm{mK})^2$ at redshifts $z=10.87,\ 9.93,\ 8.68,\ 8.37,\ 8.13,\ 7.48$. The paper argues these limits supersede all previous PAPER results, because earlier detections were inflated by signal loss documented in Cheng et al. (2018). This matters because it settles what the PAPER experiment actually constrained about cosmic reionization, and because the null-test and redundancy checks provide a template for validating redundant arrays.

What carries the argument

The machinery is the delay-spectrum estimator: visibilities from redundant 30 m baselines are Fourier transformed along frequency with a Blackman-Harris taper, cross-multiplied between baseline pairs and between even/odd day bins, and bootstrap-averaged to form $P(k_\parallel, k_\perp)$ via the paper's Equation 7. A frequency-independent top-hat fringe-rate filter suppresses the zero-fringe-rate common mode, and PRISim foreground simulations set the flux scale and supply the foreground-dependent variance $\sigma_P^2 = 2P_s P_N + P_N^2$. The omission of the delay filter and covariance weighting is the load-bearing simplification that avoids signal loss.

What would settle it

Reprocess the original, uncompressed PAPER-64 visibilities from the correlator output through calibration and LST binning from scratch; if the $z\sim10$ excess at delays $>400$ ns and the imaginary power drop to thermal levels, the upper limits here would not be the limiting uncertainty and the attribution to foregrounds plus non-redundancy would be incomplete.

Watch

Extended reading notes

Core claim

The central claim is that when PAPER-64 data are taken through a linear pipeline without foreground filtering or covariance weighting, the statistically significant high-delay power seen in earlier analyses is largely gone; the remaining excess at $|\tau| > 400$ ns tracks foregrounds modulated by LST, baseline non-redundancy, and calibration phase errors, and is not cosmological. The paper therefore reports its results as upper limits, not detections. It further claims, on the basis of signal loss documented in Cheng et al. (2018), that these upper limits supersede all earlier PAPER power spectrum limits, including PAPER-32 results and the PAPER-64 limits of Ali et al. (2015) and Ali et al. (2018).

Load-bearing premise

Everything downstream assumes the archived visibilities---already compressed, redundantly calibrated, absolutely calibrated to Pictor A, and LST-binned by earlier pipelines---are free of spectral or temporal structure injected by those steps; the paper itself states that this compression may imprint systematic biases that it does not investigate.

Editorial extensions

If this is right

  • All previous PAPER power spectrum limits are superseded, including PAPER-32 results and the Ali et al. (2015, 2018) PAPER-64 limits.
  • Constraints on the intergalactic medium spin temperature that used earlier PAPER upper limits (Pober et al. 2015; Greig et al. 2016) should be disregarded.
  • PAPER-64 provides no significant detection of the 21 cm Epoch of Reionization power spectrum; the field's best results remain upper limits.
  • Residual high-delay power is attributed to foregrounds and baseline non-redundancy, so future redundant arrays should run redundancy jackknives before cross-multiplying baselines.
  • The $z=8.37$ bin analyzed here overlaps the neighboring redshift bins, so its information is not fully independent of the $z=8.13$ and $z=8.68$ bins.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the archival compression and calibration steps imprinted spectral or temporal structure, the new upper limits could still inherit that bias; re-running from raw visibilities would separate this from sky signals.
  • The same simplified estimator could be applied to other redundant arrays: if their high-delay excess also appears mainly in the imaginary cross-power and even-odd null tests, non-redundancy rather than foreground subtraction would be implicated.
  • The reported limits sit roughly two orders of magnitude above fiducial reionization models, so they do not yet constrain astrophysics; reaching model levels requires either much longer integrations or removing the non-redundancy noise floor.
  • The fitted scale factor of $1.54\pm0.04$ between simulated and observed visibilities hints at a roughly 50 percent amplitude uncertainty in the sky model or calibration; resolving it would tighten the foreground error bars.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents a re-analysis of PAPER-64 archival data using a simplified, public power-spectrum pipeline called simpleDS. The analysis deliberately omits several steps that were shown to cause signal loss in earlier PAPER analyses (delay filtering, aggressive fringe-rate filtering, empirical covariance weighting) and uses a uniform FFT-based delay-spectrum estimator. The authors validate the pipeline with a parallel thermal-noise simulation and with PRISim foreground simulations, present multi-redshift power spectra and several null tests, and report 2-sigma upper limits on the 21 cm power spectrum at six redshifts in the range z ~ 7.5 to 10.9. They conclude that high-delay excess power is non-cosmological and that these limits supersede all previous PAPER results.

Significance. If the upper-limit construction is repaired, this would be a valuable contribution. The paper provides an independent, simplified pipeline with publicly available code; the thermal-noise simulation matches the analytic expectation to within about 30%; PRISim is used to check the absolute calibration scale and foreground error bars; and the null tests and imaginary-power diagnostics give a reasonably coherent picture that the high-delay detections are not cosmological. The cautionary message about signal loss in earlier PAPER estimators is important. However, the headline upper limits are not yet statistically valid as stated, and the uninvestigated archival compression and calibration steps weaken the 'lossless' claim.

major comments (3)
  1. [§9 / Table 2 / Abstract] The headline upper limits are constructed by taking the minimum bandpower over 0.3 < |k| < 0.6 h/Mpc and adding the 2-sigma error of that same band (e.g., Table 2, z=7.49: 5.6e4 + 3.5e4 = 9.1e4 mK^2 ≈ (300 mK)^2; z=9.93: 3.5e6 + 1.9e5 ≈ (1900 mK)^2). Because the selected minimum is an order statistic of many noisy bandpowers, it is biased low relative to a typical bandpower, and adding the selected band's own error does not restore nominal 2-sigma coverage; no trial factor or simultaneous-coverage correction is supplied. The abstract values therefore do not yet have a demonstrated statistical meaning as upper limits. Please report the full Δ²(k) curves and either quote pointwise limits at a pre-specified k with the selection protocol stated, or construct a simultaneous upper limit with an explicit multiplicity correction.
  2. [§8.2.2 / §9] Section 8.2.2 reports statistically significant even-odd null-test residuals at |τ| > 400 ns in the three highest-redshift bins (Figure 15), yet Section 9 selects the k-range 0.3 < k < 0.6 h/Mpc on the grounds that 'both null-tests pass for most k-modes in each redshift bin.' No quantitative pass criterion is defined, and the selection is made after inspecting the same data that produce the limits. Please specify the metric used to declare a null-test pass, report how many modes pass in each bin, and either exclude failing modes from the limit or propagate the null-test failure into the quoted uncertainty. Without this, the k-range selection is post hoc and the coverage of the quoted limits is unclear.
  3. [§2.2 / title / abstract] The analysis begins with archival data that were compressed, redundantly calibrated, absolutely calibrated, and LST-binned by earlier pipelines, and the text explicitly states that this compression 'may imprint systematic biases in the data but are not investigated in this work.' Since the paper is titled a 'lossless' re-analysis and the abstract and conclusion state that these limits supersede all previous PAPER results, the uninvestigated pre-pipeline steps are central to the claim. The authors should either investigate the effect of the archival compression and calibration on the final power spectra (for example, by propagating the LST-binning and compression into the simulations used for validation) or clearly qualify in the abstract and conclusions that the new pipeline is lossless only from the calibrated, LST-binned products onward, so inherited systematic biases remain a caveat.
minor comments (6)
  1. [§2.1] The observing window is given as ending on 'JD 24563745'; this appears to be a typo, likely JD 2456374 or JD 2456374.5.
  2. [Figure 4 caption] The caption contains 'the there is general agreement'; it should read 'there is general agreement.'
  3. [§5.1.1] The text has 'suppressed by the the application' and 'fringe-rate filer'; both should be corrected to 'by the application' and 'fringe-rate filter.'
  4. [§7.1.2] The text refers to 'the nose input described in Section 3'; this should be 'noise input.'
  5. [§6 / References] The citation 'lglewicz & Hoaglin (1993)' should be 'Iglewicz & Hoaglin (1993)'.
  6. [§9] The sentence 'These limit supersede all previous PAPER results' should read 'These limits supersede all previous PAPER results.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the quoted 21 cm limits are direct bandpowers from an independent uniform-FFT analysis, not outputs of a fitted model or a self-citation chain.

full rationale

The central upper limits are produced by a uniform-FFT delay transform of calibrated, LST-binned visibilities, cross-multiplication of redundant baseline pairs, and bootstrap variance estimation (Eq. 7; Sections 7 and 9). No parameter fitted to the measured cosmological bandpowers enters the quoted Δ² values. The PRISim scale factor g=1.54 is fit to visibility amplitudes but is used only for foreground error bars and null-test comparisons (Eqs. 9–10; Sections 4.2, 7.1.3, and 8), not for the upper limits themselves, so it is not a fitted input renamed as a prediction. The 1.086 signal-loss correction is derived from the fractional decrease of the foreground simulation under the fringe-rate filter, not from the measured 21 cm power spectrum. The paper's reliance on C18 for the statement that previous PAPER results suffer signal loss and are superseded is a self-citation among overlapping authors, but C18 is external published work, and the present pipeline independently removes the lossy steps (delay filtering, optimal fringe-rate filtering, empirical covariance weighting), so the central derivation does not reduce to that citation. Concerns about the Section 9 choice of the minimum bandpower plus its own 2σ error as an upper limit, and about even-odd null-test residuals at |τ|>400 ns, are statistical-validity and selection-effect issues rather than circularity under the enumerated definitions.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The central claim rests on un-re-verified archival data products, a fitted foreground simulation, and several modeling choices. Free parameters include the PRISim scale factor, the fringe-rate filter cutoff, the signal-loss correction factor, and the selected k range. No invented physical entities are introduced.

free parameters (4)
  • PRISim model scale factor g = 1.54 +/- 0.04 (95% confidence)
    Section 4.2: maximum likelihood fit of simulated visibility amplitude to observed data over LST 0.5-4.5 h and 120-180 MHz; used to scale foreground simulations for error bars and null-test comparisons.
  • Fringe-rate filter low-frequency cutoff = 3.5e-5 Hz
    Section 5.1.1: chosen by hand to exclude common-mode signals with periods longer than about 45 minutes; this filter is applied to all data and requires a signal-loss correction.
  • Signal-loss correction factor = 1.086
    Section 5.1.1: derived from the measured 7.97% power decrease of the scaled foreground simulation under the top-hat fringe-rate filter; applied to all power spectra and uncertainties, assuming the same loss applies to cosmological signal.
  • Upper-limit k range = 0.3 < |k| < 0.6 h/Mpc
    Section 9: headline limits are the minimum power estimates in this k range where both null-tests pass for most k-modes; this range is selected after inspection of the measured spectra.
assumptions (7)
  • domain assumption Delay-to-k mapping: baseline length variation over the 10 MHz band is small enough that delay mode tau maps one-to-one to k_parallel (Liu et al. 2014a).
    Invoked in Section 7, Equation 7, to interpret delay-transformed visibilities as a cosmological power spectrum P(k_parallel, k_perp). If the 30 m baseline's uv track changes significantly across the band, the mapping is approximate.
  • domain assumption Archival data products (compressed, redundantly calibrated, absolutely calibrated to Pictor A, LST-binned) are unbiased for power spectrum analysis.
    Section 2.2 states the pipeline begins with these products and that the compression process may imprint systematic biases in the data but is not investigated in this work. This is load-bearing for all final limits.
  • domain assumption The noise model uses Tsys = 180 K * (nu/180 MHz)^-2.55 + 144 K (Rogers & Bowman 2008; C18), with Gaussian, baseline-independent noise.
    Section 3 uses this model to generate the noise simulation that validates normalization and error bars. If Tsys is underestimated, the thermal error predictions are too small.
  • domain assumption The PRISim foreground sky model, scaled by g=1.54, adequately represents foreground power for error-bar and null-test interpretation.
    Section 4.2 fits g to visibility data, and Section 7.1.3 uses g^2 P_PRISim as P_s(k) in Equation 9. The paper notes PRISim amplitude accuracy is expected only to within a factor of two.
  • domain assumption The top-hat fringe-rate filter suppresses only common-mode systematics, and the 1.086 correction factor derived from the foreground simulation applies to the cosmological signal as well.
    Section 5.1.1: the filter reduces simulated foreground power by 7.97%, so all power spectra are multiplied by 1.086. There is no direct test on a cosmological signal.
  • domain assumption Noise on different baselines is independent, including baselines sharing an antenna, in the variance propagation.
    Appendix A, Equation 9 assumes independent n_i; the footnote explicitly notes shared-antenna correlations are ignored. This affects the foreground-dependent error bars used in null-test interpretation.
  • domain assumption Baselines within each redundant group measure the same sky signal up to thermal noise, after removing antennas 21 and 31.
    Section 6 measures non-redundancy far exceeding thermal noise (noise variance about 40 times smaller than the data distribution), yet the analysis proceeds with cross-multiplication of redundant pairs; bootstrap variance may not capture non-redundant systematics.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A simplified, lossless re-analysis of PAPER-64." pith.science (2026). https://pith.science/paper/LZR7SXA4

@misc{pith2026190902085,
  author       = {Pith},
  title        = {Pith review of: A simplified, lossless re-analysis of PAPER-64},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LZR7SXA4}},
  note         = {Machine review of arXiv:1909.02085}
}
abstract

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).

Figures

Figures reproduced from arXiv: 1909.02085 by the authors.

Figure 1
Figure 1. Comparison between the prior PAPER analysis by Ali et al. (2015) and “simpleDS”. The frequency independent fringe rate filter has a smoother delay response compared to the one used in A15 and C18 in order to reduce leakage of foreground power outside the wedge. The delay filter for foreground removal has been omitted from this analysis to keep the pipeline as simple as possible. While the foreground removal techniqu… view at source ↗
Figure 2
Figure 2. The antenna positions of PAPER-64. Highlighted are the three baseline types used in this analysis. These baselines consist of East-West baselines from adjacent antenna columns with no row separation (e.g. 49-41, 1-4, 0-26), baselines with one column separation and one positive Northward row separation (e.g. 10-41, 1-48, 0-38), and baselines with one column separation and one negative Northward row separation (e.g. 4… view at source ↗
Figure 3
Figure 3. The six frequency bands used in this analysis plotted over the relative occupancy of flags from RFI. Redshift bands are denoted by the Blackman-Harris window functions used during the Fourier transform from frequency to delay in order to reduce foreground leakage to high delays. The specific windows chosen here are centered on z = 10.87, 9.93, 8.68, 8.13, and 7.48 (119.7, 130.0, 146.7, 155.6, and 167.5 MHz respectiv… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: An LST, frequency plot of the amplitude of a rep￾resentative observed visibilities (left) and the PRISim simu￾lation (right) for the ∼ 8 hours of data analyzed in this work. While many details in the visibility amplitude structure do not match, the there is general agr…
Figure 5
Figure 5. Figure 5: The ratio of observed (left) and uncertainty (right) to simulated visibility amplitude notice the difference in color scales. The observed is obtained by an unweighted averaged over all baselines and the uncertainty from the variance across baselines. Also plotted are …
Figure 6
Figure 6. Figure 6: A comparison of the Top-Hat fringe-rate filter (TH, left) and the filter used in C18 (right) in the fringe￾rate, frequency domain. The C18 filter varies with frequency and this spectral variation can cause additional structure when performing a delay transform of the v…
Figure 7
Figure 7. Figure 7: Top: LST and frequency waterfalls of represen￾tative baselines taken from the even LST binned set before (left) and after (right) application of the Top Hat FRF. The baseline illustrated is the antenna pairs (1,4). The applica￾tion of the fringe-rate filter removes ver…
Figure 8
Figure 8. Figure 8: A representative Median Absolute Deviation (MAD) for both data (left) and noise simulation (right) com￾puted for each time and frequency observed by PAPER in the LST range 00h 30m00s − 08h 36m00s . The data shown here corresponds to strictly East-West baselines in [PI…
Figure 9
Figure 9. Figure 9: A histogram of modified z-scores of data averaged in quadrature over LST day (even/odd) (black line) and input noise simulation also averaged over LST day (orange) before (left) and after (right) averaging in quadrature over frequencies and times. Also plotted is the d…
Figure 10
Figure 10. Figure 10: The ratio of the bootstrap error bars of both data and noise to estimates of the predicted uncertainties for each redshift bin. Panels are ordered such that redshift increases towards the upper left. A ratio helps to compare different estimates of power spectrum error…
Figure 11
Figure 11. Figure 11: Power spectrum estimates computed for the observed data (black), simulated noise (orange), and simulated observation (blue). Error bars on points are the bootstrapped uncertainty. The solid green line indicates the theoretical thermal noise estimate for each redshift …
Figure 12
Figure 12. Figure 12: The estimated power spectrum value before (purple) and after (black) application of the fringe-rate filter. The simulated data points (blue) have also been filtered with the FRF (the same as in [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: The real (black) and imaginary (red) components of the power spectrum of PAPER data. The red shaded region is the foreground dependent theoretical errorbar drawn around the imaginary components; all other lines are the same as in [PITH_FULL_IMAGE:figures/full_fig_p01…
Figure 14
Figure 14. Figure 14: Null-tests constructed by splitting the LST range ([00h 30m00s , 08h 36m00s )) in half (at 04h 30m), making two power spectrum estimates and differencing the result. Real (black) and imaginary (red) are both shown, along with the null-test results when applied to the …
Figure 15
Figure 15. Figure 15: In the LST binning process, data were split and binned into sets containing only even or odd numbered days; plotted here is the difference between the power spectra from these two sets. We use the same color scheme as [PITH_FULL_IMAGE:figures/full_fig_p021_15.png]
Figure 16
Figure 16. Figure 16: The dimensionless power spectrum (∆2 (k) = k 3 2π2 P(|k|)) estimates and their uncertainties derived from the PAPER￾64 observations. All error bars represent 2σ uncertainties. Also plotted are the theoretical thermal noise limits from Equation 8 (solid green) and the …
Figure 17
Figure 17. Figure 17: A comparison of the lowest limits achieved by various instruments in the k-ranges reported by each instrument. The results reported from this paper are taken in the range 0.3 ≤ k ≤ 0.6 h Mpc−1 . Data is taken from the MWA (stars; Dillon et al. (2014, 2015), Beardsley …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Intensity fluctuations of radio halo in galaxy cluster: Insights from power spectrum estimation

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    Angular power spectra of 610 MHz radio halos show excess power-law fluctuations only in Abell 2744, requiring multiplicative C_ℓ ∝ ℓ^{-3} structure atop an exponential profile and consistent with ICM turbulence.

  2. The EoR 21-cm Bispectrum at $z=8.2$ from MWA data I: Foregrounds and preliminary upper limits

    astro-ph.CO 2025-07 conditional novelty 6.0 of 10

    Using 17 minutes of MWA data, the authors place the first comprehensive upper limits on the z=8.2 21-cm bispectrum and characterize its foreground wedge.

  3. A Visibility-based 21 cm Bispectrum Estimator for Radio-interferometric Data

    astro-ph.CO 2025-06 conditional novelty 6.0 of 10

    A gridded-visibility, FFT-accelerated estimator for the binned 21 cm multi-frequency angular bispectrum and 3D bispectrum is presented and validated against simulations with an input bispectrum model.

  4. A visibility-based angular bispectrum estimator for radio-interferometric data

    astro-ph.CO 2024-12 conditional novelty 6.0 of 10

    An FFT-based estimator recovers the angular bispectrum from gridded radio-interferometric visibilities across all triangle shapes, validated on simulated MWA data at 154.25 MHz.

  5. Investigating radio source population $\&$ spatial characteristics of diffuse Galactic synchrotron emission in the GAMA-23 field with uGMRT Band-3

    astro-ph.GA 2026-08 conditional novelty 4.0 of 10

    A 325 MHz uGMRT mosaic of the GAMA-23 field yields a 5741-source catalog, source counts, spectral indices, and angular power spectra of diffuse Galactic synchrotron emission.

Reference graph

Works this paper leans on

67 extracted references · 17 canonical work pages · cited by 5 Pith papers

  1. [2]

    S., Parsons, A

    Ali, Z. S., Parsons, A. R., Zheng, H., et al. 2015, ApJ, 809, 61, doi: 10.1088/0004-637X/809/1/61 —. 2018, ApJ, 863, 201, doi: 10.3847/1538-4357/aad7b4

    doi:10.1088/0004-637x/809/1/61 2015

  2. [3]

    2010, ArXiv e-prints

    Andrae, R. 2010, ArXiv e-prints. https://arxiv.org/abs/1009.2755 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, aap, 558, A33, doi: 10.1051/0004-6361/201322068

    arXiv 2010

  3. [4]

    2001, PhR, 349, 125, doi: 10.1016/S0370-1573(01)00019-9 —

    Barkana, R., & Loeb, A. 2001, PhR, 349, 125, doi: 10.1016/S0370-1573(01)00019-9 —. 2007, Reports on Progress in Physics, 70, 627, doi: 10.1088/0034-4885/70/4/R02 26 Kolopanis et al

    doi:10.1016/s0370-1573(01)00019-9 2001

  4. [5]

    P., Hazelton, B

    Beardsley, A. P., Hazelton, B. J., Sullivan, I. S., et al. 2016, ApJ, 833, 102, doi: 10.3847/1538-4357/833/1/102

    doi:10.3847/1538-4357/833/1/102 2016

  5. [6]

    G., Brentjens, M

    Bernardi, G., de Bruyn, A. G., Brentjens, M. A., et al. 2009, A&A, 500, 965, doi: 10.1051/0004-6361/200911627

    doi:10.1051/0004-6361/200911627 2009

  6. [7]

    G., Harker, G., et al

    Bernardi, G., de Bruyn, A. G., Harker, G., et al. 2010, A&A, 522, A67+, doi: 10.1051/0004-6361/200913420

    doi:10.1051/0004-6361/200913420 2010

  7. [8]

    J., Mitchell, D

    Bernardi, G., Greenhill, L. J., Mitchell, D. A., et al. 2013, ApJ, 771, 105, doi: 10.1088/0004-637X/771/2/105

    doi:10.1088/0004-637x/771/2/105 2013

  8. [9]

    Bernardi, G., Zwart, J. T. L., Price, D., et al. 2016, MNRAS, 461, 2847, doi: 10.1093/mnras/stw1499

    work page doi:10.1093/mnras/stw1499 2016

Show all 67 references
  1. [10]

    D., & Rogers, A

    Bowman, J. D., & Rogers, A. E. E. 2010, Nature, 468, 796, doi: 10.1038/nature09601

    2010 doi

  2. [11]

    R., Kolopanis, M., et al

    Cheng, C., Parsons, A. R., Kolopanis, M., et al. 2018, ApJ, 868, 26, doi: 10.3847/1538-4357/aae833

    2018 doi

  3. [12]

    Clark, B. G. 1999, in Astronomical Society of the Pacific Conference Series, Vol. 180, Synthesis Imaging in Radio Astronomy II, ed. G. B. Taylor, C. L. Carilli, & R. A. Perley, 1–+

    1999

  4. [13]

    D., Condon, J

    Cotton, W. D., Condon, J. J., Perley, R. A., et al. 2004, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5489, Ground-based Telescopes, ed. J. Oschmann, Jacobus M., 180–189 de Oliveira-Costa, A., Tegmark, M., Gaensler, B. M., et al. 2008, ...

    2004

  5. [14]

    R., Parsons, A

    DeBoer, D. R., Parsons, A. R., Aguirre, J. E., et al. 2016, ArXiv e-prints. https://arxiv.org/abs/1606.07473

    2016 arXiv

  6. [15]

    S., Liu, A., Williams, C

    Dillon, J. S., Liu, A., Williams, C. L., et al. 2014, PhRvD, 89, 023002, doi: 10.1103/PhysRevD.89.023002

    2014 doi

  7. [16]

    S., Neben, A

    Dillon, J. S., Neben, A. R., Hewitt, J. N., et al. 2015, Phys. Rev. D, 91, 123011, doi: 10.1103/PhysRevD.91.123011

    2015 doi

  8. [17]

    S., Kohn, S

    Dillon, J. S., Kohn, S. A., Parsons, A. R., et al. 2018, MNRAS, 477, 5670, doi: 10.1093/mnras/sty1060

    2018 doi

  9. [18]

    1994, An Introduction to the

    Efron, B., & Tibshirani, R. 1994, An Introduction to the

    1994

  10. [19]

    R., Oh, S

    Furlanetto, S. R., Oh, S. P., & Briggs, F. H. 2006, PhR, 433, 181, doi: 10.1016/j.physrep.2006.08.002

    2006 doi

  11. [20]

    S., & Chengalur, J

    Ghosh, A., Bharadwaj, S., Ali, S. S., & Chengalur, J. N. 2011, MNRAS, 418, 2584, doi: 10.1111/j.1365-2966.2011.19649.x G´ orski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759, doi: 10.1086/427976

    2011

  12. [21]

    Greig, B., Mesinger, A., & Pober, J. C. 2016, MNRAS, 455, 4295, doi: 10.1093/mnras/stv2618

    2016 doi

  13. [22]

    2017, HERA-Team/pyuvdata: Version 1.2, doi: 10.5281/zenodo.1044022

    Hazelton, B., Beardsley, A., Pober, J., et al. 2017, HERA-Team/pyuvdata: Version 1.2, doi: 10.5281/zenodo.1044022. https://doi.org/10.5281/zenodo.1044022 H¨ ogbom, J. A. 1974, A&AS, 15, 417

    2017 doi

  14. [23]

    R., Hancock, P

    Hurley-Walker, N., Callingham, J. R., Hancock, P. J., et al. 2017, MNRAS, 464, 1146, doi: 10.1093/mnras/stw2337

    2017 doi

  15. [24]

    T., van der Tol, S., Cotton, W

    Intema, H. T., van der Tol, S., Cotton, W. D., et al. 2009, A&A, 501, 1185, doi: 10.1051/0004-6361/200811094

    2009 doi

  16. [25]

    C., Parsons, A

    Jacobs, D. C., Parsons, A. R., Aguirre, J. E., et al. 2013, ApJ, 776, 108, doi: 10.1088/0004-637X/776/2/108

    2013 doi

  17. [26]

    C., Pober, J

    Jacobs, D. C., Pober, J. C., Parsons, A. R., et al. 2015, ApJ, 801, 51, doi: 10.1088/0004-637X/801/1/51

    2015 doi

  18. [27]

    C., Hazelton, B

    Jacobs, D. C., Hazelton, B. J., Trott, C. M., et al. 2016, ApJ, 825, 114, doi: 10.3847/0004-637X/825/2/114

    2016 doi

  19. [28]

    2001–, SciPy: Open source scientific tools for Python

    Jones, E., Oliphant, T., Peterson, P., et al. 2001–, SciPy: Open source scientific tools for Python. http://www.scipy.org/

    2001

  20. [29]

    C., Trott, C

    Joseph, R. C., Trott, C. M., & Wayth, R. B. 2018, AJ, 156, 285, doi: 10.3847/1538-3881/aaec0b

    2018 doi

  21. [30]

    R., Pober, J

    Kerrigan, J. R., Pober, J. C., Ali, Z. S., et al. 2018, ApJ, 864, 131, doi: 10.3847/1538-4357/aad8bb lglewicz, B., & Hoaglin, D. C. 1993, Volume 16: How to Detect and Handle Outliers, ed. P. Edward F. Mykytka (The ASQC Basic References in Quality Control: Statistical Techniques)

    2018 doi

  22. [31]

    Liu, A., & Parsons, A. R. 2016, MNRAS, 457, 1864, doi: 10.1093/mnras/stw071

    2016 doi

  23. [32]

    R., & Trott, C

    Liu, A., Parsons, A. R., & Trott, C. M. 2014a, Phys. Rev. D, 90, 023018, doi: 10.1103/PhysRevD.90.023018 —. 2014b, Phys. Rev. D, 90, 023019, doi: 10.1103/PhysRevD.90.023019

    doi

  24. [34]

    Loeb, A., & Furlanetto, S. R. 2013, The First Galaxies in the Universe (Princeton University Press)

    2013

  25. [35]

    2015, MNRAS, 446, 3478, doi: 10.1093/mnras/stu2310

    McKinley, B., Yang, R., L´ opez-Caniego, M., et al. 2015, MNRAS, 446, 3478, doi: 10.1093/mnras/stu2310

    2015 doi

  26. [36]

    2007, in Astronomical Society of the Pacific Conference Series, Vol

    Golap, K. 2007, in Astronomical Society of the Pacific Conference Series, Vol. 376, Astronomical Data Analysis Software and Systems XVI, ed. R. A. Shaw, F. Hill, & D. J. Bell, 127

    2007

  27. [37]

    Mellema, G., Koopmans, L. V. E., Abdalla, F. A., et al. 2013, Experimental Astronomy, 36, 235, doi: 10.1007/s10686-013-9334-5

    2013 doi

  28. [38]

    2011, MNRAS, 411, 955, doi: 10.1111/j.1365-2966.2010.17731.x

    Mesinger, A., Furlanetto, S., & Cen, R. 2011, MNRAS, 411, 955, doi: 10.1111/j.1365-2966.2010.17731.x

    2011

  29. [39]

    F., Aguirre, J

    Moore, D. F., Aguirre, J. E., Kohn, S. A., et al. 2017, ApJ, 836, 154, doi: 10.3847/1538-4357/836/2/154

    2017 doi

  30. [40]

    F., & Wyithe, J

    Morales, M. F., & Wyithe, J. S. B. 2010, ARA&A, 48, 127, doi: 10.1146/annurev-astro-081309-130936 Simplified analysis of PAPER-64 27

    2010 doi

  31. [41]

    Jones, M. E. 2010, in Phased Array Systems and Technology (ARRAY), 2010 IEEE International Symposium on, IEEE, 690–694

    2010

  32. [42]

    Oh, S. P. 2001, ApJ, 553, 499, doi: 10.1086/320957

    2001 doi

  33. [43]

    2006–, NumPy: A guide to NumPy, USA: Trelgol Publishing

    Oliphant, T. 2006–, NumPy: A guide to NumPy, USA: Trelgol Publishing. http://www.numpy.org/

    2006

  34. [44]

    G., Bandura, K., et al

    Paciga, G., Albert, J. G., Bandura, K., et al. 2013, MNRAS, 433, 639, doi: 10.1093/mnras/stt753

    2013 doi

  35. [45]

    2012a, ApJ, 753, 81, doi: 10.1088/0004-637X/753/1/81

    Aguirre, J. 2012a, ApJ, 753, 81, doi: 10.1088/0004-637X/753/1/81

    doi

  36. [46]

    R., & Backer, D

    Parsons, A. R., & Backer, D. C. 2009, AJ, 138, 219, doi: 10.1088/0004-6256/138/1/219

    2009 doi

  37. [47]

    R., Liu, A., Ali, Z

    Parsons, A. R., Liu, A., Ali, Z. S., & Cheng, C. 2016, ApJ, 820, 51, doi: 10.3847/0004-637X/820/1/51

    2016 doi

  38. [48]

    R., Pober, J

    Parsons, A. R., Pober, J. C., Aguirre, J. E., et al. 2012b, ApJ, 756, 165, doi: 10.1088/0004-637X/756/2/165

    doi

  39. [49]

    R., Backer, D

    Parsons, A. R., Backer, D. C., Foster, G. S., et al. 2010, AJ, 139, 1468, doi: 10.1088/0004-6256/139/4/1468

    2010 doi

  40. [50]

    R., Liu, A., Aguirre, J

    Parsons, A. R., Liu, A., Aguirre, J. E., et al. 2014, ApJ, 788, 106, doi: 10.1088/0004-637X/788/2/106

    2014 doi

  41. [51]

    H., Yatawatta, S., Koopmans, L

    Patil, A. H., Yatawatta, S., Koopmans, L. V. E., et al. 2017, ApJ, 838, 65, doi: 10.3847/1538-4357/aa63e7

    2017 doi

  42. [52]

    2015, ApJ, 801, 138, doi: 10.1088/0004-637X/801/2/138

    Patra, N., Subrahmanyan, R., Sethi, S., Udaya Shankar, N., & Raghunathan, A. 2015, ApJ, 801, 138, doi: 10.1088/0004-637X/801/2/138

    2015 doi

  43. [53]

    C., Parsons, A

    Pober, J. C., Parsons, A. R., Jacobs, D. C., et al. 2012, AJ, 143, 53, doi: 10.1088/0004-6256/143/2/53

    2012 doi

  44. [54]

    C., Parsons, A

    Pober, J. C., Parsons, A. R., Aguirre, J. E., et al. 2013, ApJL, 768, L36, doi: 10.1088/2041-8205/768/2/L36

    2013 doi

  45. [55]

    C., Liu, A., Dillon, J

    Pober, J. C., Liu, A., Dillon, J. S., et al. 2014, ApJ, 782, 66, doi: 10.1088/0004-637X/782/2/66

    2014 doi

  46. [56]

    C., Ali, Z

    Pober, J. C., Ali, Z. S., Parsons, A. R., et al. 2015, ApJ, 809, 62, doi: 10.1088/0004-637X/809/1/62

    2015 doi

  47. [57]

    C., Hazelton, B

    Pober, J. C., Hazelton, B. J., Beardsley, A. P., et al. 2016, ApJ, 819, 8, doi: 10.3847/0004-637X/819/1/8

    2016 doi

  48. [58]

    R., & Loeb, A

    Pritchard, J. R., & Loeb, A. 2010, PhRvD, 82, 023006, doi: 10.1103/PhysRevD.82.023006

    2010 doi

  49. [59]

    Rogers, A. E. E., & Bowman, J. D. 2008, AJ, 136, 641, doi: 10.1088/0004-6256/136/2/641

    2008 doi

  50. [60]

    G., Cooray, A., & Knox, L

    Santos, M. G., Cooray, A., & Knox, L. 2005, ApJ, 625, 575, doi: 10.1086/429857

    2005 doi

  51. [61]

    E., Wayth, R

    Sokolowski, M., Tremblay, S. E., Wayth, R. B., et al. 2015, PASA, 32, 4, doi: 10.1017/pasa.2015.3

    2015 doi

  52. [62]

    S., Morales, M

    Sullivan, I. S., Morales, M. F., Hazelton, B. J., et al. 2012, ApJ, 759, 17, doi: 10.1088/0004-637X/759/1/17

    2012 doi

  53. [63]

    2019, PRISim: Precision Radio Interferometry Simulator (for radio astronomy applications), 0.2-alpha, Zenodo, doi: 10.5281/zenodo.2548117

    Thyagarajan, N., Kolopanis, M., Jacobs, D., Murray, S., & santoshmh. 2019, PRISim: Precision Radio Interferometry Simulator (for radio astronomy applications), 0.2-alpha, Zenodo, doi: 10.5281/zenodo.2548117. https://doi.org/10.5281/zenodo.2548117

    2019 doi

  54. [64]

    C., Bowman, J

    Thyagarajan, N., Jacobs, D. C., Bowman, J. D., et al. 2015a, ApJL, 807, L28, doi: 10.1088/2041-8205/807/2/L28 —. 2015b, ApJ, 804, 14, doi: 10.1088/0004-637X/804/1/14

    doi

  55. [65]

    J., Goeke, R., Bowman, J

    Tingay, S. J., Goeke, R., Bowman, J. D., et al. 2013, PASA, 30, 7, doi: 10.1017/pasa.2012.007

    2013 doi

  56. [66]

    B., & L´ opez-Cruz, O

    Peterson, J. B., & L´ opez-Cruz, O. 2014, ApJL, 782, L9, doi: 10.1088/2041-8205/782/1/L9

    2014 doi

  57. [67]

    B., Lenc, E., Bell, M

    Wayth, R. B., Lenc, E., Bell, M. E., et al. 2015, PASA, 32, e025, doi: 10.1017/pasa.2015.26

    2015 doi

  58. [68]

    G., Brentjens, M

    Yatawatta, S., de Bruyn, A. G., Brentjens, M. A., et al. 2013, A&A, 550, A136, doi: 10.1051/0004-6361/201220874

    2013 doi

  59. [69]

    2014, MNRAS, 445, 1084, doi: 10.1093/mnras/stu1773 APPENDIX A

    Zheng, H., Tegmark, M., Buza, V., et al. 2014, MNRAS, 445, 1084, doi: 10.1093/mnras/stu1773 APPENDIX A. FOREGROUND DEPENDENT VARIANCE To find the variance of P (k), being by assuming each visibility ˜Vi(τ,u,v,w ) = s +ni is the sum of the true sky signal, s , and a noise compon...

    2014 doi

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.