Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

Mitigating antenna gain errors with HyFoReS in CHIME simulations

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read HyFoReS, a two-step foreground filter, estimates and subtracts antenna gain errors from simulated 21-cm data, reducing power-spectrum foreground bias by up to three orders of magnitude and below the noise floor for small bandpass errors.

desk verdict Useful, honest extension of HyFoReS to gain errors in polarized visibilities; the headline below-noise claim is only proven for smooth foregrounds because point sources are switched off. read the letter →

arxiv 2506.09170 v1 pith:6PCXHKCB submitted 2025-06-10 astro-ph.IM astro-ph.COgr-qc

Haochen Wang , Panupong Phoompuang , Kiyoshi W. Masui , Arnab Chakraborty , Simon Foreman This is my paper
classification astro-ph.IMastro-ph.COgr-qc
keywords 21-cmintensitymappingforegroundsubtractionHyFoReSantennagaincalibrationCHIMEpowerspectrumbiasKarhunen-Loèvefilterpolarizedvisibilities
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

21-cm intensity mapping tries to map cosmic hydrogen, but radio foregrounds swamp the signal by orders of magnitude, and telescope gain errors imprint spectral structure on the foregrounds so that standard linear filters no longer remove them. This paper tests HyFoReS, an algorithm that first applies a linear foreground filter and then cross-correlates the filtered signal with a foreground-dominated data estimate to isolate the gain-coupled residual emission and subtract it. In CHIME-style simulations, HyFoReS brings foreground bias in the power spectrum below thermal noise for bandpass gain errors at the $10^{-5}$ and $10^{-4}$ level, and reduces the bias by roughly three orders of magnitude for larger bandpass errors and for complex antenna-dependent gains. The result matters because gain calibration errors at the percent level are realistic, and the same machinery works in visibility space or map space for any parametrizable systematic.

What carries the argument

The central object is the window matrix $W$, which maps the true gain perturbations into the gains that the cross-correlation step actually measures; HyFoReS pseudoinverts $W$ to recover the gain modes that survive the KL filter, and uses those unwindowed gains to subtract foreground residuals. For complex antenna gains, each gain is split into real and imaginary parts with derivative matrices $\Gamma$ and $\Delta = i\Gamma$, the window matrix is built as a block matrix coupling the two parts, and only the XX and YY co-polarization visibilities enter the fit because cross-polarization foregrounds are too weak to estimate reliably. The argument runs on the foreground-signal hierarchy (foregrounds dominate by roughly $10^5$) and on treating gains as small, so that first-order perturbation theory suffices and the cleaned signal carries only second-order foreground residuals.

What would settle it

Repeat the identical bandpass-error simulation with point sources included in the sky and in the KL filter's foreground covariance: if the cleaned power spectrum retains foreground bias well above the noise floor at $10^{-4}$ gains, the premise that filtered residuals are dominated by gain-coupled foregrounds is false. As a separate check, verify that post-cleaning bias scales with the square of the injected gain amplitude; a linear scaling would mean the gain estimator itself is biased rather than limited by second-order terms.

Watch

Extended reading notes

Core claim

The paper's claim is that HyFoReS can estimate antenna gain errors directly from the data, without a sky model, and subtract the foregrounds those errors leak into the 21-cm signal. After the KL foreground filter has removed the intrinsic smooth foregrounds, the residual in the filtered data is dominated by foregrounds multiplied by gain perturbations; HyFoReS cross-correlates that residual with the unfiltered visibilities, a good foreground proxy because foregrounds outshine the signal by a factor of about $10^5$, to estimate the gains, corrects the estimates through the pseudoinverse of a window matrix that accounts for modes the KL filter removed, and subtracts the first-order foreground residual. For frequency-only bandpass errors this leaves the power spectrum unbiased at the noise level for RMS perturbations of $10^{-5}$ and $10^{-4}$, while $10^{-3}$ and $10^{-2}$ level errors are suppressed by about three orders of magnitude. For complex antenna-dependent gains, the real and imaginary parts are estimated separately and co-polarization visibilities alone are used; the bias is again cut by three orders of magnitude, with the residual set by noise in the gain estimates and by second-order ($G^2 v_F$) foreground terms.

Load-bearing premise

The method assumes the linear foreground filter has already removed the smooth galactic emission almost completely, a condition the simulations secure only by deleting point sources from the sky model because the filter cannot handle them.

Editorial extensions

If this is right

  • Bandpass gain errors at RMS $10^{-4}$ or below no longer bias the 21-cm power spectrum above thermal noise after HyFoReS, so calibration tolerances at those frequencies can be relaxed.
  • At the percent-level gain errors typical of calibrated interferometers, foreground bias is still reduced by about three orders of magnitude, improving the prospects for detecting the 21-cm auto-power spectrum.
  • Because the method requires only a linear foreground filter and a foreground-dominated data estimate, it carries over to any parametrizable, time-independent systematic and to map-space analyses, not just the gain cases demonstrated here.
  • The residual after cleaning is dominated by second-order gain-foreground coupling and by noise in the gain estimates, so further gains would require iterative cleaning or noise-filtered gain estimators.

Reading between the lines

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

  • The simulations omit point sources because the KL filter's foreground covariance does not model them, so the tested sky is smoother than the real one; on real data, point-source residuals could violate the premise that only gain-coupled foregrounds survive the linear filter, and the authors' ongoing point-source model work will determine how much this degrades performance.
  • The noise floor in gain estimation scales with the dimensionality of the gain parameter space, so reducing that space, for example by exploiting smooth frequency dependence of per-feed gains or longer time-stationarity, could extend HyFoReS' sub-noise performance to the antenna-dependent case.
  • An iterative or Wiener-filtered variant of HyFoReS, which the paper suggests, could be tested directly in these simulations: if residuals currently scale as $G^2 v_F$, a second pass should reduce them further unless noise in the first-pass gain estimates dominates.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. This paper extends the HyFoReS systematics-foreground-removal formalism to gain-type errors in simulated CHIME data. After reviewing the general algorithm, in which a linear foreground filter produces a signal estimate dominated by systematics-coupled foregrounds and a foreground estimate is cross-correlated with it to estimate the perturbation parameters, the authors treat bandpass errors and, more elaborately, complex antenna-dependent gains on stacked visibilities. The complex case requires separate real and imaginary derivative matrices, a block window matrix, and a restriction to co-polarization modes. The numerical tests inject gain errors with RMS amplitudes from 1e-5 to 1e-2 into full- and half-pathfinder telescope simulations and measure the two-dimensional HI power-spectrum bias before and after HyFoReS. The paper reports below-thermal-noise foreground suppression for bandpass errors at 1e-5 and 1e-4, and roughly three orders-of-magnitude suppression for 1e-3 and 1e-2 bandpass or antenna errors. It explicitly lists second-order gain terms, noise in the gain estimator, the exclusion of point sources from the KL foreground covariance, and the sparse baseline coverage of the half-pathfinder as limitations.

Significance. The paper is a solid, transparent simulation study. Its main strengths are that it tests the estimator on injected gains rather than fitting the power spectrum, it explicitly verifies the noise-gain term in Eq. (49) against simulation, it handles the non-commutativity of complex conjugation and the KL filter by splitting gains into real and imaginary parts, and it reports quantitative suppression factors with the limitations stated in the text. The claimed thresholds (e.g., below-thermal-noise performance for 1e-4 bandpass errors) are concrete and testable. If the approach survives inclusion of realistic point-source foregrounds, it would be a useful extension of a method already demonstrated on CHIME beam errors. The point-source exclusion is the principal reason the results do not yet establish the abstract's broad claim for real 21-cm telescopes.

major comments (2)
  1. [Sec. III A; Eq. (5); Sec. V C] Point sources are removed from the simulations because the KL foreground filter does not include a point-source covariance, but this directly controls the regime in which the central approximation holds. Eq. (5) states that after the linear filter the estimated signal is dominated by the systematics-coupled foreground, KvF << vHI; the gain estimator in Eq. (10) and the subtraction in Eq. (15) are derived under that condition. On the real radio sky, point sources are spectrally smooth and are not represented in the KL covariance used here, so the filter will not remove them as it removes the diffuse synchrotron component. An unmodeled point-source term K vF would enter the estimated signal and the cross-correlations in Eqs. (7) and (10), biasing the gain estimates and leaving point-source leakage that the subtraction in Eq. (15) cannot distinguish from the HI signal. The abstract and Sec. VI extend the conclusions to '21-cm telescopes' and 'real CHIME telescope data' without this caveat. The manuscript should either (i) include a point-source term in the KL covariance and rerun the central tests, (ii) measure the degradation when point sources are added without modifying the filter, or (iii) explicitly restrict the headline claims to diffuse Galactic synchrotron foregrounds and state that point-source leakage is not yet quantified.
  2. [Sec. IV B; Fig. 4; Sec. V A] The antenna-dependent-gain test on the half-pathfinder telescope has an unperturbed power spectrum that already shows roughly an order-of-magnitude foreground bias at low k_perp (Fig. 4, top-left panel), attributed to sparse baseline sampling. As a result, the residual low-k_perp bias in the cleaned panels for the 1e-3 and 1e-2 cases may be the telescope's intrinsic alias floor rather than gain-induced leakage that HyFoReS failed to remove. The quoted three-order suppression is a before/after statement within this configuration, and it does not by itself show that HyFoReS brings the spectrum to the level that would be achieved by a perfect-gain version of the same telescope. The paper should quantify the residual relative to the unperturbed spectrum (for example, (P_cleaned - P_unperturbed)/sigma) and report how much of the residual at low k_perp would persist with zero gain errors.
minor comments (5)
  1. [Table I; Sec. IV B] The half-pathfinder integration time listed as 18,250 days (50 years) is surprising; please clarify whether this is an effective integration after redundant-baseline stacking or a typo, because the noise-gain floor in Sec. IV B and the comparison in Sec. V A depend on the actual integration time.
  2. [Figs. 2 and 4] The color scale is saturated and uses the same range for all panels; because the key claim is 'bias below the one-sigma error', the unperturbed and cleaned panels would be easier to evaluate with a symmetric scale and visible colorbar limits.
  3. [Eq. (16); Sec. IV B] Equation (16) summarizes the cleaned signal as vHI + G^2 vF, but the noise-gain term identified in Eq. (47) is not shown; adding it to the display, or explicitly saying it is omitted for clarity, would make the later discussion in Sec. IV B more direct.
  4. [Eq. (44)] The sentence describing Eq. (44) says the pseudoinverse is taken first and then rows and columns for cross-polarizations are zeroed, but the equation notation could also be read as a pseudoinverse of the restricted matrix; since these operations do not commute in general, please specify the exact order.
  5. [Abstract; Sec. IV A] The abstract states that bandpass perturbations are reduced below thermal noise when the RMS is on the order of 1e-4 or lower, but the body of the paper qualifies this as 'over most k bins' with the wedge region excepted; the abstract should carry the same qualification.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the gains are injected and recovered against known signal, and the core equations are re-derived rather than imported.

full rationale

The paper's derivation chain is self-contained. Visibilities are modeled as vd = (I+G)(vHI+vF); a linear KL filter K produces the signal estimate v̂HI ≈ KGvF, and the foreground estimate is taken to be the data (A=I). The gain estimator (Eq. 7) and window matrix (Eqs. 10-12) are derived in the paper, not assumed from the cited HyFoReS papers; the only borrowed design choice (Ei ∝ Γi, Di = Ei†Ei) is attributed to [32] but is tested directly here through injection-and-recovery of known gains. The headline results are numerical outcomes of a simulation with injected gains and a known true HI field: the cleaned power spectrum is compared with the fiducial spectrum, and no parameter is fitted to the power spectrum being 'predicted.' The acknowledged approximations—using the perturbed data as the foreground estimate in Eq. (12), which creates second-order G^2vF residuals, and noise in gain estimates (Eq. 47)—are explicit limitations, not concealed inputs. The removal of point sources from the foreground model (Sec. III A, V C) weakens the realism of the test and the applicability of the condition KvF << vHI to the real sky, but this is a correctness/robustness concern, not a circular construction: the equations do not define the result in terms of the claimed output. Self-citations to [32] and [33] are contextual and not load-bearing because the present paper re-derives the estimator and validates it against simulations. No circular step can be exhibited.

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

The algorithm introduces no fitted free parameters or new physical entities. It rests on foreground dominance, multiplicative small time-independent gains, a specific decomposition of the KL filter, and a foreground model limited to synchrotron; the results should be read with those assumptions.

assumptions (4)
  • domain assumption The 21-cm foreground emission dominates the HI signal by about 10^5, so the observed visibilities v_d can be used as the foreground estimate in the HyFoReS cross-correlations.
    Invoked in Sec. II B around Eq. (28) and in Sec. II A; if foregrounds are not dominant, the gain estimates inherit signal and noise contamination.
  • domain assumption Gain errors are multiplicative, small, and time-independent, so first-order perturbation theory applies and RA-stationarity of the error model holds.
    Stated in Sec. II B ('we assume that the gain errors are time independent') and in the introduction of Sec. II; the algorithm only subtracts first-order residuals and second-order terms limit performance.
  • domain assumption The KL foreground filter is linear over the reals, is distributive over complex parameters through K1 and K2, and adequately removes intrinsic foregrounds, KvF << vHI.
    Used in Eqs. (17)-(18) and after Eq. (5); if the filter's foreground covariance is wrong, the residual is not dominated by KGvF.
  • ad hoc to paper Point sources are excluded from the simulation; the foreground is only Galactic synchrotron.
    Sec. III A: 'we turn off the point sources and only use the Galactic synchrotron emission as the foreground model for this study.' This is a simplification adopted because the KL filter lacks a point-source covariance.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mitigating antenna gain errors with HyFoReS in CHIME simulations." pith.science (2026). https://pith.science/paper/6PCXHKCB

@misc{pith2026250609170,
  author       = {Pith},
  title        = {Pith review of: Mitigating antenna gain errors with HyFoReS in CHIME simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6PCXHKCB}},
  note         = {Machine review of arXiv:2506.09170}
}
abstract

Hybrid Foreground Residual Subtraction (HyFoReS) is a new family of algorithms designed to remove systematics-induced foreground contamination for 21-cm intensity mapping data. Previously, the algorithm was shown to be effective in mitigating beam perturbations in sky maps from the Canadian Hydrogen Intensity Mapping Experiment (CHIME). In this study, we apply HyFoReS to CHIME simulations and test the algorithm's ability to mitigate antenna gain-type systematics in polarized visibilities. Simulating a two-cylinder telescope similar to the CHIME pathfinder, we find that HyFoReS reduces foreground bias caused by bandpass perturbations to a level below the thermal noise, provided that the RMS value of the perturbations is on the order of $10^{-4}$ or lower. When tested with complex antenna-dependent gain errors, HyFoReS can reduce residual foreground bias in the power spectrum by up to three orders of magnitude. While noise bias and second-order perturbations are currently the limiting factors for the algorithm, we have demonstrated that HyFoReS can suppress gain-induced foreground leakage in polarized data from 21-cm telescopes, aiding in the detection of the 21-cm auto-power spectrum for hydrogen intensity mapping experiments.

Figures

Figures reproduced from arXiv: 2506.09170 by the authors.

Figure 1
Figure 1. FIG. 1. Flow chart showing the key steps in the default [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Bias in the two-dimensional power spectra of the simulated data with the full-pathfinder telescope setting. The top [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Comparison between the true 10 [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Same as Fig. 2 but implemented using the half-pathfinder setting with complex antenna-dependent errors. Due to [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Comparison between the true visibility errors from antenna-dependent gains (blue), estimated visibility errors (orange), [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison between the error in the estimated visibility gains from the 10 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. The differences between the unwindowed visibility gain estimates and true visibility gains for the case of 10 [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization

    astro-ph.IM 2026-07 accept novelty 2.5 of 10

    A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.

Reference graph

Works this paper leans on

36 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    Chang, U.-L

    T.-C. Chang, U.-L. Pen, J. B. Peterson, and P. McDon- ald, Baryon acoustic oscillation intensity mapping of dark energy, Phys. Rev. Lett. 100, 091303 (2008)

    work page 2008

  2. [2]

    Loeb and J

    A. Loeb and J. S. B. Wyithe, Possibility of precise mea- surement of the cosmological power spectrum with a ded- icated survey of 21 cm emission after reionization, Phys. Rev. Lett. 100, 161301 (2008)

    work page 2008

  3. [3]

    Amiri, K

    M. Amiri, K. Bandura, A. Boskovic, T. Chen, J.-F. Cliche, M. Deng, N. Denman, M. Dobbs, M. Fandino, S. Foreman, M. Halpern, D. Hanna, A. S. Hill, G. Hin- shaw, C. H¨ ofer, J. Kania, P. Klages, T. L. Landecker, J. MacEachern, K. Masui, J. Mena-Parra, N. Milutinovic, A. Mirhosseini, L. Newburgh, R. Nitsche, A. Ordog, U.- L. Pen, T. Pinsonneault-Marotte, A. ...

    work page 2022

  4. [4]

    Chakraborty, A

    A. Chakraborty, A. Datta, N. Roy, S. Bharadwaj, T. R. Choudhury, K. K. Datta, S. Pal, M. Choudhury, S. Choudhuri, P. Dutta, and D. Sarkar, First multi- redshift limits on post–epoch of reionization 21 cm signal from z = 1.96–3.58 using ugmrt, The Astrophysical Jour- nal Letters 907, L7 (2021)

    work page 2021

  5. [5]

    Zhang, Y

    M. Zhang, Y. Li, J.-F. Zhang, and X. Zhang, H I intensity mapping with MeerKAT: forecast for delay power spec- trum measurement using interferometer mode, Monthly Notices of the Royal Astronomical Society 524, 2420 (2023), arXiv:2301.04445 [astro-ph.CO]

    arXiv 2023

  6. [6]

    Vanderlinde, A

    K. Vanderlinde, A. Liu, B. Gaensler, D. Bond, G. Hin- shaw, C. Ng, C. Chiang, I. Stairs, J.-A. Brown, J. Sievers, J. Mena, K. Smith, K. Bandura, K. Masui, K. Spekkens, L. Belostotski, M. Dobbs, N. Turok, P. Boyle, M. Ru- pen, T. Landecker, U.-L. Pen, and V. Kaspi, The Cana- dian Hydrogen Observatory and Radio-transient Detec- tor (CHORD), in Canadian Long...

    arXiv 2019

  7. [7]

    L. B. Newburgh, K. Bandura, M. A. Bucher, T. C. Chang, H. C. Chiang, J. F. Cliche, R. Dav´ e, M. Dobbs, C. Clarkson, K. M. Ganga, T. Gogo, A. Gumba, N. Gupta, M. Hilton, B. Johnstone, A. Karastergiou, M. Kunz, D. Lokhorst, R. Maartens, S. Macpherson, M. Mdlalose, K. Moodley, L. Ngwenya, J. M. Parra, J. Peterson, O. Recnik, B. Saliwanchik, M. G. San- tos, ...

    arXiv 2016

  8. [8]

    A. A. Costa, R. G. Landim, C. P. Novaes, L. Xiao, E. G. M. Ferreira, F. B. Abdalla, B. Wang, E. Abdalla, R. A. Battye, A. Marins, C. A. Wuensche, L. Barosi, F. A. Brito, A. R. Queiroz, T. Villela, K. S. F. Fornazier, V. Liccardo, L. Santos, M. V. dos Santos, and J. Zhang, The bingo project: Vii. cosmological forecasts from 21 cm intensity mapping, Astrono...

    work page 2022

Show all 36 references
  1. [9]

    D. R. DeBoer, A. R. Parsons, J. E. Aguirre, et al. , Hy- drogen epoch of reionization array (HERA), The Publi- cations of the Astronomical Society of the Pacific 129, 045001 (2017)

    2017

  2. [10]

    A. H. Patil, S. Yatawatta, L. V. E. Koopmans, et al. , 17 Upper limits on the 21 cm epoch of reionization power spectrum from one night with LOF AR, The Astrophysi- cal Journal 838, 65 (2017)

    2017

  3. [11]

    M. W. Eastwood, M. M. Anderson, R. M. Monroe, G. Hallinan, B. R. Barsdell, S. A. Bourke, M. A. Clark, S. W. Ellingson, J. Dowell, H. Garsden, L. J. Greenhill, J. M. Hartman, J. Kocz, T. J. W. Lazio, D. C. Price, F. K. Schinzel, G. B. Taylor, H. K. Vedantham, Y. Wang, and D. P....

    2018

  4. [12]

    Barry, M

    N. Barry, M. Wilensky, C. M. Trott, et al. , Improving the epoch of reionization power spectrum results from murchison widefield array season 1 observations, The As- trophysical Journal 884, 1 (2019)

    2019

  5. [13]

    Ghara, T

    R. Ghara, T. R. Choudhury, K. K. Datta, and S. Choud- huri, Imaging the redshifted 21 cm pattern around the first sources during the cosmic dawn using the SKA, Monthly Notices of the Royal Astronomical Society 464, 2234 (2016)

    2016

  6. [14]

    Collaboration, M

    C. Collaboration, M. Amiri, K. Bandura, A. Chakraborty, M. Dobbs, M. Fandino, S. Fore- man, H. Gan, M. Halpern, A. S. Hill, G. Hinshaw, C. H¨ ofer, T. L. Landecker, Z. Li, J. MacEachern, K. Masui, J. Mena-Parra, N. Milutinovic, A. Mirhos- seini, L. Newburgh, A. Ordog, S. Paul,...

    2023 arXiv

  7. [15]

    Amiri, K

    M. Amiri, K. Bandura, T. Chen, M. Deng, M. Dobbs, M. Fandino, S. Foreman, M. Halpern, A. S. Hill, G. Hinshaw, C. H¨ ofer, J. Kania, T. L. Landecker, J. MacEachern, K. Masui, J. Mena-Parra, N. Miluti- novic, A. Mirhosseini, L. Newburgh, A. Ordog, U.-L. Pen, T. Pinsonneault-Maro...

    2023

  8. [16]

    L. Wolz, A. Pourtsidou, K. W. Masui, T.-C. Chang, J. E. Bautista, E.-M. M¨ uller, S. Avila, D. Bacon, W. J. Percival, S. Cunnington, C. Anderson, X. Chen, J.-P. Kneib, Y.-C. Li, Y.-W. Liao, U.-L. Pen, J. B. Peter- son, G. Rossi, D. P. Schneider, J. Yadav, and G.-B. Zhao, HI co...

    2021

  9. [17]

    Cunnington, Y

    S. Cunnington, Y. Li, M. G. Santos, J. Wang, I. P. Carucci, M. O. Irfan, A. Pourtsidou, M. Spinelli, L. Wolz, P. S. Soares, C. Blake, P. Bull, B. Engelbrecht, J. Fon- seca, K. Grainge, and Y.-Z. Ma, Hi intensity map- ping with meerkat: power spectrum detection in cross- correl...

    2022

  10. [18]

    L.-C. Li, L. Staveley-Smith, and J. Rhee, An hi intensity mapping survey with a phased array feed, Research in Astronomy and Astrophysics 21, 030 (2021)

    2021

  11. [19]

    C. J. Anderson, N. J. Luciw, Y. C. Li, C. Y. Kuo, J. Ya- dav, K. W. Masui, T. C. Chang, X. Chen, N. Opper- mann, Y. W. Liao, U. L. Pen, D. C. Price, L. Staveley- Smith, E. R. Switzer, P. T. Timbie, and L. Wolz, Low- amplitude clustering in low-redshift 21-cm intensity maps cro...

    2018 arXiv

  12. [20]

    K. W. Masui, E. R. Switzer, N. Banavar, K. Bandura, C. Blake, L.-M. Calin, T.-C. Chang, X. Chen, Y.-C. Li, Y.-W. Liao, A. Natarajan, U.-L. Pen, J. B. Peterson, J. R. Shaw, and T. C. Voytek, Measurement of 21 cm brightness fluctuations at z ∼ 0.8 in cross-correlation, The Astro...

    2013

  13. [21]

    Chang, U.-L

    T.-C. Chang, U.-L. Pen, K. Bandura, and J. B. Peterson, An intensity map of hydrogen 21-cm emission at redshift z˜0.8, Nature (London) 466, 463 (2010)

    2010

  14. [22]

    E. R. Switzer, K. W. Masui, K. Bandura, L.-M. Calin, T.- C. Chang, X.-L. Chen, Y.-C. Li, Y.-W. Liao, A. Natara- jan, U.-L. Pen, J. B. Peterson, J. R. Shaw, and T. C. Voytek, Determination of z 0.8 neutral hydrogen fluc- tuations using the 21 cm intensity mapping autocorrela- t...

    2013

  15. [23]

    Abdurashidova, J

    Z. Abdurashidova, J. E. Aguirre, P. Alexander, Z. S. Ali, Y. Balfour, A. P. Beardsley, G. Bernardi, T. S. Billings, J. D. Bowman, R. F. Bradley, P. Bull, J. Burba, S. Carey, C. L. Carilli, C. Cheng, D. R. DeBoer, M. Dex- ter, E. de Lera Acedo, T. Dibblee-Barkman, J. S. Dil- lo...

    2022

  16. [24]

    S. Paul, M. G. Santos, Z. Chen, and L. Wolz, A first detection of neutral hydrogen intensity mapping on mpc scales at z ≈ 0.32 and z ≈ 0.44 (2023), arXiv:2301.11943 [astro-ph.CO]

    2023 arXiv

  17. [25]

    J. R. Bond, Signal-to-noise eigenmode analysis of the two-year cobe maps, Phys. Rev. Lett. 74, 4369 (1995)

    1995

  18. [26]

    Ewall-Wice, N

    A. Ewall-Wice, N. Kern, J. S. Dillon, A. Liu, A. Par- sons, S. Singh, A. Lanman, P. L. Plante, N. Fagnoni, E. d. L. Acedo, D. R. DeBoer, C. Nunhokee, P. Bull, T.- C. Chang, T. J. W. Lazio, J. Aguirre, and S. Weinberg, DAYENU: a simple filter of smooth foregrounds for in- tensi...

    2020

  19. [27]

    J. R. Shaw, K. Sigurdson, U.-L. Pen, A. Stebbins, and M. Sitwell, All-sky interferometry with spherical har- monic transit telescopes, The Astrophysical Journal 781, 57 (2014)

    2014

  20. [28]

    J. R. Shaw, K. Sigurdson, M. Sitwell, A. Stebbins, and U.-L. Pen, Coaxing cosmic 21 cm fluctuations from the polarized sky using m-mode analysis, Phys. Rev. D 91, 083514 (2015). 18

    2015

  21. [29]

    Byrne, M

    R. Byrne, M. F. Morales, B. J. Hazelton, and M. Wilen- sky, A unified calibration framework for 21 cm cosmology, Monthly Notices of the Royal Astronomical Society 503, 2457–2477 (2021)

    2021

  22. [30]

    J. L. Sievers, Calibration of quasi-redundant interferom- eters (2017)

    2017

  23. [31]

    Ewall-Wice, J

    A. Ewall-Wice, J. S. Dillon, B. Gehlot, A. Parsons, T. Cox, and D. C. Jacobs, Precision calibration of radio interferometers for 21 cm cosmology with no redundancy and little knowledge of antenna beams and the radio sky (2021)

    2021

  24. [32]

    H. Wang, J. Mena-Parra, T. Chen, and K. Masui, Remov- ing systematics-induced 21-cm foreground residuals by cross-correlating filtered data, Phys. Rev. D 106, 043534 (2022)

    2022

  25. [33]

    H. Wang, K. Masui, K. Bandura, A. Chakraborty, M. Dobbs, S. Foreman, L. Gray, M. Halpern, A. Joseph, J. MacEachern, J. Mena-Parra, K. Miller, L. Newburgh, S. Paul, A. Reda, P. Sanghavi, S. Siegel, and D. Wulf, Demonstration of hybrid foreground removal on chime data (2024), ar...

    2024 arXiv

  26. [34]

    Wijnholds and A.-J

    S. Wijnholds and A.-J. van der Veen, Multisource self- calibration for sensor arrays, IEEE Transactions on Sig- nal Processing 57, 3512 (2009)

    2009

  27. [35]

    C. G. T. Haslam, U. Klein, C. J. Salter, H. Stoffel, W. E. Wilson, M. N. Cleary, D. J. Cooke, and P. Thomasson, A 408 MHz all-sky continuum survey. I - Observations at southern declinations and for the North Polar region, A&A 100, 209 (1981)

    1981

  28. [36]

    Bandura, G

    K. Bandura, G. E. Addison, M. Amiri, J. R. Bond, D. Campbell-Wilson, L. Connor, J.-F. Cliche, G. Davis, M. Deng, N. Denman, M. Dobbs, M. Fandino, K. Gibbs, A. Gilbert, M. Halpern, D. Hanna, A. D. Hincks, G. Hinshaw, C. H¨ ofer, P. Klages, T. L. Landecker, K. Masui, J. Mena Par...

    2014

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.