Pith. sign in

REVIEW 4 major objections 7 minor 56 references

Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer

T0 review · 4 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read This paper forecasts that a Moon-based radio interferometer with about 6,500 m² of collecting area can detect the high-redshift radio-loud quasars needed for 21-cm forest studies at z≈6, and that an SKA-scale lunar array pushes those…

desk verdict A useful design forecast for a lunar low-frequency array, but the headline redshift limits rest on an unquantified QLF extrapolation and the abstract overstates the small-array reach. read the letter →

arxiv 2504.15086 v2 pith:EWXWPCH2 submitted 2025-04-21 astro-ph.CO gr-qchep-ph

Siyuan Zhang , Qi Niu , Yichao Li , Xin Zhang This is my paper · ORCID
classification astro-ph.COgr-qchep-ph
keywords 21-cmforesthigh-redshiftradio-loudquasarsMoon-basedinterferometerEpochofReionizationquasarluminosityfunctionradioarrayconfigurationlow-frequencysurveysSKA-scale
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 works out how large a low-frequency radio interferometer on the far side of the Moon must be to find the high-redshift radio-loud quasars that 21-cm forest observations need as background light sources. Using a physical quasar luminosity function extended with a dust-obscuration correction, it forecasts detectable source counts in a 10,000 square-degree, one-year survey for both broad-band continuum mode and high-resolution 21-cm forest mode. The central numbers are that an array of eight 40 m stations, roughly 6,500 m², already produces statistically usable quasar samples in continuum mode out to z≈10, while an SKA-scale array with 512 stations reaches z≈10 in 21-cm forest mode and z≈16 in continuum mode. A larger 2048-station array, exceeding one square kilometer, extends the forest-mode limit to z≈11. The paper also shows that splitting the same collecting area into more, smaller stations improves survey speed, at the cost of sharply higher data and processing demands.

What carries the argument

The machinery is a physical-driven quasar luminosity function folded through the radio interferometer noise equation. The luminosity function starts from a halo mass function, assigns each halo a central black hole through a mass relation, converts black hole mass to quasar activity with an Eddington-limited duty cycle fitted to observed luminosity functions at z≈6 and z≈6.8, assumes that 10 percent of quasars are radio-loud, and uses an observed radio-loudness distribution to integrate the fraction whose radio flux exceeds a given threshold. That threshold is set at ten times the array noise variance, which is computed from system temperature, effective station area, number of stations, bandwidth, and integration time, so each array configuration maps directly onto predicted quasar counts. An X-ray-based obscuration correction modifies the optical luminosity function before the radio-loud fraction is applied.

What would settle it

A deep 150 MHz survey covering 10,000 square degrees down to roughly ten microjansky would count radio-loud quasars at z≈8–10; if those counts fall well below the predicted curves in the paper's Figures 2 and 4, the extrapolated luminosity function is falsified. A second, independent check is to measure the obscured quasar fraction at z>5 from X-ray-selected active galactic nuclei; a steep rise with redshift would break the flat-obscuration assumption.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes configuration thresholds for a lunar low-frequency array: for a 10,000 deg² survey with one year of integration, detection of high-redshift radio-loud quasars at ten times the noise level requires roughly 6,500 m² of collecting area at z≈6, about 419,000 m² in an SKA-scale layout to reach z≈10 in 21-cm forest mode and z≈16 in continuum mode, and more than one square kilometer to reach z≈11 in forest mode. It also finds that holding the total collecting area fixed while increasing the station count and shrinking station diameter, for example 8192 stations of 10 m instead of 512 stations of 40 m, raises the number of detected quasars and the redshift limit by widening the field of view, with the trade-off appearing in data transmission and computation. An additional result is that a maximum baseline of at least about 25 km keeps source confusion below thermal noise for the configurations considered. The obscuration-corrected luminosity function produces a flatter faint end and partially closes the gap between the model and observed luminosity functions at z≈6 and z≈6.8.

Load-bearing premise

The calculation leans on extrapolating the abundance of radio-loud quasars from redshifts 6 to 7 out to redshifts 10 to 16, assuming the same 10 percent radio-loud share and the same dust obscuration at every redshift; if radio-loud quasars become rarer at high redshift than this model says, the required array sizes are too small and the redshift limits are too high.

Editorial extensions

If this is right

  • A lunar array with only eight 40 m stations can already provide a statistically meaningful sample of radio-loud quasars out to z≈10 in continuum mode, enough to begin selecting 21-cm forest background sources at z≈6.
  • At SKA scale, the 21-cm forest survey itself detects background quasars out to z≈10, meaning the reionization era is reachable without first requiring a separate ultra-deep continuum survey.
  • Continuum mode with an SKA-scale array reaches z≈16, extending quasar counts to the edge of Cosmic Dawn.
  • Going from 512 to 2048 stations buys roughly one additional redshift unit in 21-cm forest mode, from z≈10 to z≈11.
  • Reconfiguring a fixed collecting area into more, smaller stations increases the number of detectable quasars and the redshift limit, but data rates and processing load grow with the number of baselines.
  • A maximum baseline of roughly 25 km is sufficient to keep source confusion below thermal noise at 200 MHz for all array sizes considered.

Reading between the lines

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

  • If future X-ray or infrared surveys show that the obscured quasar fraction grows toward z>6, the faint-end counts here are optimistic and the collecting-area thresholds should be read as lower bounds.
  • The same forecasting machinery could be applied directly to a ground-based SKA-scale array, so SKA-Low could empirically test the predicted quasar counts at z≈6–10 before any lunar construction, and those counts would anchor the lunar design.
  • The paper counts quasars per redshift bin but does not simulate the 21-cm absorption spectra themselves; the actual constraining power on the intergalactic medium will also depend on line widths and optical depths, so a detected sample of roughly ten sources per bin may yield fewer usable forest sightlines.
  • The engineering result on station diameter suggests an optimization frontier: for a fixed collecting area, the scientifically best layout is many small stations, but the data-rate limit may ultimately set the practical station number for a lunar observatory.
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

4 major / 7 minor

Summary. The paper investigates the array configuration requirements for a future Moon-based low-frequency interferometer to detect high-redshift radio-loud quasars (HzRLQs) that could serve as background sources for 21-cm forest observations. The authors extend a previously developed quasar luminosity function (QLF) by including an X-ray-based obscuration correction, then combine this with a standard interferometer sensitivity formula to forecast the number of detectable HzRLQs for various configurations. Two survey modes are considered: a continuum survey with 10 MHz bandwidth and a 21-cm forest survey with 5 kHz spectral resolution, assuming 10^4 deg^2 of sky coverage and one year of observation. The main quantitative results are that a collecting area of about 6500 m^2 enables detection at z~6, an SKA-like array with N_st=512 extends the detection limit to z~10 for the 21-cm forest mode and z~16 for the continuum mode, and arrays with N_st=2048 reach z~11 in the 21-cm forest mode. The paper also estimates the confusion limit and concludes that a maximum baseline of about 25 km is sufficient to make confusion negligible.

Significance. If the forecasts hold, the paper provides useful first-order engineering guidance for a lunar low-frequency array and identifies a plausible path to obtaining the high-redshift radio-loud quasar sample needed for 21-cm forest studies during the Epoch of Reionization. The sensitivity calculation is transparent and standard, the QLF fitting procedure is clearly described, and the authors explicitly acknowledge several limitations, including the redshift-independent obscuration fraction and the extrapolation of the QLF to z>6. The paper is a forecast, not a measurement, so its value depends on the realism of the input assumptions; nevertheless, the configuration comparison is a sensible and timely contribution.

major comments (4)
  1. [§II.B, Eq. (3), Figs. 2–5] The detection counts in Eq. (3) are linear in the assumed 10% radio-loud fraction and in (1−f_obsc), and the QLF is fitted only at z=6 and 6.8 before being extrapolated to z=10–16, with f_obsc calibrated from z≲5 X-ray samples and assumed redshift-independent. No uncertainty or sensitivity analysis is presented for these inputs, so the headline redshift limits (z∼10 for 21-cm forest, z∼16 for continuum) are conditional point values. For example, decreasing the radio-loud fraction from 10% to 3%, a change within the range allowed by current sparse high-z samples, reduces predicted counts by a factor of about 3 and shifts the N=10 crossing to lower redshift. Please add a robustness test or clearly qualify all point-value claims in the abstract and conclusions.
  2. [Abstract and §IV.B] The abstract states that 'a minimum collecting area of ~6500 m^2 enables detection at z~6,' but §IV.B reports that for the same eight-station configuration (A_eff≈6547 m²) the 21-cm forest survey becomes nearly impossible beyond z>5. The abstract does not specify which survey mode this statement refers to; please reconcile or clarify the mode and detection criterion so that the abstract is not internally inconsistent.
  3. [§IV.C, Eq. (11)] The confusion-limit estimate uses a single power-law fit (C=3.94, β=−1.07) with R²=0.997 but no quoted uncertainties, and adopts m=30 from Hogg (2001) without discussion of its applicability to the synthesized-beam and brightness-temperature regime considered here. Since the required baseline length is a headline engineering requirement (≳25 km), please propagate the fit errors and show the dependence of the required baseline on m, or justify m for this context.
  4. [§III, Eq. (7)] The calculation does not state how the fixed total observing time (1 yr) is allocated among pointings for each configuration. Because the field of view changes with station diameter, the per-pointing integration time Δt in Eq. (7) differs among the scenarios in Figs. 3 and 5; without this information the comparison between configurations mixes sensitivity with survey speed. Please give the tiling formula and any survey-efficiency factor used.
minor comments (7)
  1. [§IV.C] The section heading reads 'C. onfusion limit'; it should be 'C. Confusion limit.'
  2. [§II.B, Eq. (4)] The text introduces 'φ0 = 0.73' but the equation uses ψ0; please define the relationship between φ0 and ψ0 and include units for the parameters.
  3. [§II.B] The phrase 'least-square values' should be 'chi-square values' or 'sum of squared residuals' with the degrees of freedom stated; as written, the values 7.79 and 3.78 are not interpretable.
  4. [§IV.A] The text refers to a 'gray histogram' for the N_st=8 configuration, but Figure 2 displays lines, not histograms; please correct the wording.
  5. [Fig. 1 caption] The caption should state explicitly that the first two faint-end data points are omitted from the fit; currently this information appears only in the main text.
  6. [§IV.C] The power-law fit expression N=3.94 S^{−1.07} lacks units for N and S; please specify per square degree and Jy, respectively.
  7. [§V] The conclusion that the obscuration-corrected QLF yields 'more accurate predictions' overstates what can be claimed given the redshift-independent obscuration assumption; consider softening this to 'partially alleviates,' consistent with the caveat in §II.B.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the HzRLQ forecasts are an extrapolation of an externally calibrated QLF combined with a standard sensitivity calculation, not a fit to the predicted quantity.

full rationale

The paper's central result—detection limits for HzRLQs under different Moon-based array configurations—follows from Eq. (7), the standard interferometer noise formula, applied to number counts from Eq. (3). Those counts are the product of a halo mass function, a duty cycle, a 10% radio-loud fraction, and a radio-loudness distribution. The underlying QLF is not defined in terms of the detection limits or the number counts it is used to forecast. In Section II.A the paper states that 'the quasar duty cycle D_q = t_q/t_H(z)... constrain t_q by fitting observational data of the QLF at high redshifts [30, 31]', and Section II.B reports 'The QLF model is fitted to the data by adjusting the mean quasar lifetime t_q until the likelihood function is maximized. Two main datasets are used in this work, i.e., one set of data is at z~6 [30], and the other one is at z~6.8 [31].' These are external optical measurements (Matsuoka et al.), not outputs of the present forecast. The only self-citation to the group's earlier model, [18], is load-bearing in the sense that the model is adopted from that paper, but the cited model is itself calibrated against external QLF data and is falsifiable outside the present paper; this is independent support rather than circularity. The high-redshift behavior beyond z=6.8 is an extrapolation, and the paper explicitly acknowledges the associated uncertainty: 'Due to the limited observational data available for quasars at higher redshifts, the redshift evolution of the obscuration fraction remains uncertain. In this work, we therefore adopt a simplified approach and assume a redshift-independent obscuration fraction, acknowledging that this may introduce some uncertainties in our predictions.' An extrapolation with acknowledged model uncertainty is a correctness/robustness issue, not a reduction of the prediction to its inputs. The confusion-limit estimate uses a power-law fit to source counts generated from the same QLF, but this is an internal consistency calculation for array design (Eqs. 10–11) rather than a claim that the QLF is confirmed by those counts. No equation in the paper contains the target detection redshift or N>10 threshold as an input; the threshold is applied to the output of the sensitivity calculation. Therefore there is no self-definitional, fitted-input-renamed-as-prediction, or self-citation-chain circularity at the level of the paper's central claims.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The forecast depends on the prior QLF model from Niu et al. (2025), the new redshift-independent obscuration correction, and standard interferometer sensitivity assumptions; none of these are independently verified in this paper.

free parameters (4)
  • Quasar lifetime t_q = 10^5.79 yr (z=6); 10^5.76 yr (z=6.8)
    Fit to the QLF data at z~6 and z~6.8 in Section II.B; it sets the normalization of the quasar duty cycle and therefore the predicted source counts.
  • Source count power law C = 3.94 (per deg^2 for S in Jy)
    Best-fit amplitude of N(>S)=C S^β in Section IV.C, fitted to the simulated source counts; used in the confusion limit calculation.
  • Source count slope β = -1.07
    Best-fit slope of N(>S)=C S^β in Section IV.C, fitted to the simulated source counts; used in the confusion limit calculation.
  • Detection threshold factor = 10 (dimensionless)
    The paper takes 10 times the noise variance as the flux threshold for 'detection' (Section IV); this is a chosen value that directly sets the detection limits and is not derived from data.
assumptions (6)
  • domain assumption Each dark matter halo hosts a galaxy with a central black hole, with the BH mass following Eq (1).
    Introduced in Section II.A; the BH mass function is the foundation of the QLF model.
  • domain assumption Quasar activity is Eddington-limited and characterized by a duty cycle D_q = t_q/t_H(z).
    Section II.A; needed to convert the BH mass function into an active quasar population.
  • domain assumption 10% of quasars are radio-loud at all redshifts.
    Section II.A, based on observational experience but applied over the full redshift range without evolution.
  • domain assumption The obscuration fraction f_obsc is redshift-independent, with parameters from X-ray selected AGN samples at z<5.
    Section II.B; the paper explicitly acknowledges the redshift evolution is uncertain and assumes a constant value.
  • domain assumption The sky temperature follows T_sky = 2.73 + 25.2(408/ν)^2.75 K on the Moon.
    Section III, Eq (9); this Earth-based approximation is adopted for the lunar environment.
  • domain assumption The survey covers 10^4 deg^2 in 1 year, with per-pointing integration time set by the field of view.
    Section III; these choices set the noise level and are not varied across most results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer." pith.science (2026). https://pith.science/paper/EWXWPCH2

@misc{pith2026250415086,
  author       = {Pith},
  title        = {Pith review of: Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EWXWPCH2}},
  note         = {Machine review of arXiv:2504.15086}
}
abstract

The 21-cm forest offers a powerful cosmological probe of the thermal history and small-scale structure of the intergalactic medium during the Epoch of Reionization (EoR). Its success, however, critically depends on the availability of high-redshift radio-loud quasars (HzRLQs) as background sources. In this work, we investigate the configuration requirements for a Moon-based low-frequency radio interferometer aimed at maximizing the detection of HzRLQs for future 21-cm forest studies. Building upon a previously developed quasar luminosity function (QLF), we forecast HzRLQ abundances under various array configurations. Assuming a total survey area of $10^4\,\mathrm{deg}^2$ and 1 year of observation, we compare continuum surveys with 10 MHz bandwidth and 21-cm forest surveys with 5 kHz resolution. Our results show that a minimum collecting area of $\sim$6 500 m$^2$ enables detection at $z \sim 6$, while SKA-like arrays ($N_{\mathrm{st}} = 512$) extend the detection limit to $z \sim 10$ for 21-cm forest survey and $z \sim 16$ for continuum survey. Larger arrays with $N_{\mathrm{st}} = 2048$ can reach $z \sim 11$ in 21-cm forest mode. We also explore configurations that maintain fixed collecting areas while increasing the number to enhance survey efficiency. This boosts source detection but significantly increases the data volume and computational demands. These results underscore the importance of optimizing array design for different survey goals and balancing sensitivity, spectral resolution, and data management. A well-designed Moon-based array could open a new observational window on reionization and early cosmic structure formation.

Figures

Figures reproduced from arXiv: 2504.15086 by the authors.

Figure 1
Figure 1. FIG. 1: Comparison of the best-fit QLF with and without considering [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2: The number of HzRLQs detected with continuum survey [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. FIG. 5: Similar to Figure [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figures from the paper (1 more)
Figure 6
Figure 6. Figure 6: FIG. 6: Power-law fitting of source counts at 5 [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 24 canonical work pages

  1. [1]

    Quasars are driven by the active behavior of BHs at the centers of galaxies

    Calculating the black hole (BH) mass function. Quasars are driven by the active behavior of BHs at the centers of galaxies. Therefore, the first step is to determine the abundance of BHs in galaxy centers. We use a dark matter halo mass function in Sheth–Tormen form [28] to describe the abundance of halos of different masses at different redshifts. We ass...

    work page

  2. [2]

    The quasar duty cycle𝐷q= 𝑡q/𝑡𝐻(𝑧) quantifies the fraction of black holes in the quasar phase, where 𝑡q is the quasar lifetime and 𝑡𝐻(𝑧) is the cosmic age at redshift 𝑧

    Determining the quasar duty cycle with quasar lu- minosity function (QLF). The quasar duty cycle𝐷q= 𝑡q/𝑡𝐻(𝑧) quantifies the fraction of black holes in the quasar phase, where 𝑡q is the quasar lifetime and 𝑡𝐻(𝑧) is the cosmic age at redshift 𝑧. Assuming Eddington- limited accretion, we constrain𝑡q by fitting observational data of the QLF at high redshifts [30, 31]

    work page

  3. [3]

    We assume that 10% of quasars are radio-loud [accord- ing to observational experience, e.g

    Determining the fraction of radio-loud quasars whose radio flux exceeds the required threshold𝐹𝑡ℎ . We assume that 10% of quasars are radio-loud [accord- ing to observational experience, e.g. 32–34]. To account for the radio flux distribution of the quasar population at a given optical luminosity, we adopt the observed dis- tribution of radio-loudness 𝑅 ≡...

    work page

  4. [4]

    Estimate the abundance of HzRLQs. The abundance of HzRLQs is then estimated by integrating the weighted halo mass function, 𝑁(Δ𝑧,ΔΩ,𝐹 th) = ∫ Δ𝑧,ΔΩ d𝑉 ∫ d𝑀h10%𝐷q𝜖(𝑀h,𝐹 th)Φh(𝑀h), (3) where Δ𝑧 and ΔΩ are the redshift bin and survey sky area, d𝑉 is the cosmological volume element, and Φh(𝑀h)= d𝑛/d𝑀h represents the halo mass function. B. Obscuration-correcte...

    work page 2025

  5. [5]

    J. R. Pritchard and A. Loeb, Physical Review D 78, 103511 (2008), 0802.2102

    arXiv 2008

  6. [6]

    C. L. Carilli, N. Y. Gnedin, and F. Owen, The Astrophysical Journal 577, 22 (2002), astro-ph/0205169

    arXiv 2002

  7. [7]

    Shimabukuro, K

    H. Shimabukuro, K. Ichiki, S. Inoue, and S. Yokoyama, Physical Review D 90, 083003 (2014), 1403.1605

    arXiv 2014

  8. [8]

    Thyagarajan, The Astrophysical Journal 899, 16 (2020), 2006.10070

    N. Thyagarajan, The Astrophysical Journal 899, 16 (2020), 2006.10070

    arXiv 2020

Show all 56 references
  1. [9]

    Y. Shao, Y. Xu, Y. Wang, W. Yang, R. Li, X. Zhang, and X. Chen, Nature Astronomy 7, 1116 (2023), 2307.04130

    2023 arXiv

  2. [10]

    Lopez-Honorez, O

    L. Lopez-Honorez, O. Mena, ´A. Molin´e, S. Palomares-Ruiz, and A. C. Vincent, Journal of Cosmology and Astroparticle Physics 2016, 004 (2016), 1603.06795

    2016 arXiv

  3. [11]

    S. R. Furlanetto, S. P. Oh, and F. H. Briggs, Physics Reports 433, 181 (2006), astro-ph/0608032

    2006 arXiv

  4. [12]

    J. R. Pritchard and A. Loeb, Reports on Progress in Physics 75, 086901 (2012), 1109.6012

    2012 arXiv

  5. [13]

    Shao, G.-H

    Y. Shao, G.-H. Du, T.-N. Li, and X. Zhang, Physics Letters B 862, 139342 (2025), 2501.00769

    2025 arXiv

  6. [14]

    Shimabukuro, Y

    H. Shimabukuro, Y. Xu, and Y. Shao, arXiv e-prints arXiv:2504.14656 (2025), 2504.14656

    2025

  7. [15]

    ˇSoltinsk´ y, J

    T. ˇSoltinsk´ y, J. S. Bolton, N. Hatch, M. G. Haehnelt, L. C. Keating, G. Kulkarni, E. Puchwein, J. Chardin, and D. Aubert, Monthly Notices of the Royal Astronomical Society 506, 5818 (2021), 2105.02250

    2021 arXiv

  8. [16]

    ˇSoltinsk´ y, J

    T. ˇSoltinsk´ y, J. S. Bolton, M. Molaro, N. Hatch, M. G. Haehnelt, L. C. Keating, G. Kulkarni, and E. Puchwein, Monthly No- tices of the Royal Astronomical Society 519, 3027 (2023), 2211.07655

    2023 arXiv

  9. [17]

    T.-Y. Sun, Y. Shao, Y. Li, Y. Xu, H. Wang, and X. Zhang, Communications Physics 8, 220 (2025), 2407.14298

    2025 arXiv

  10. [18]

    Shao, T.-Y

    Y. Shao, T.-Y. Sun, M.-L. Zhao, and X. Zhang, arXiv e-prints arXiv:2411.17094 (2024), 2411.17094

    2024 arXiv

  11. [19]

    X.-B. Wu, F. Wang, X. Fan, W. Yi, W. Zuo, F. Bian, L. Jiang, I. D. McGreer, R. Wang, J. Yang, et al., Nature518, 512 (2015), 1502.07418

    2015 arXiv

  12. [20]

    X. Ding, M. Onoue, J. D. Silverman, Y. Matsuoka, T. Izumi, M. A. Strauss, K. Jahnke, C. L. Phillips, J. Li, M. Volonteri, et al., Nature 621, 51 (2023), 2211.14329

    2023 arXiv

  13. [21]

    Zhang, T

    Y. Zhang, T. An, A. Wang, S. Frey, L. I. Gurvits, K.´E. Gab´anyi, K. Perger, and Z. Paragi, Astronomy and Astrophysics 662, L2 (2022), 2205.05859

    2022 arXiv

  14. [22]

    Q. Niu, Y. Li, Y. Xu, H. Guo, and X. Zhang, The Astrophysical Journal 978, 145 (2025), 2407.18136

    2025 arXiv

  15. [23]

    Lazio, C

    J. Lazio, C. Carilli, J. Hewitt, S. Furlanetto, and J. Burns, in UV/Optical/IR Space Telescopes: Innovative Technologies and Concepts IV, edited by H. A. MacEwen and J. B. Breckinridge (2009), vol. 7436 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Se...

    2009

  16. [24]

    C. W. James, R. D. Ekers, J. ´Alvarez-Mu˜niz, J. D. Bray, R. A. McFadden, C. J. Phillips, R. J. Protheroe, and P. Roberts, Phys- ical Review D 81, 042003 (2010), 0911.3009

    2010 arXiv

  17. [25]

    B. T. Indermuehle, L. Harvey-Smith, M. Marquarding, and J. Reynolds, in Observatory Operations: Strategies, Processes, and Systems VII (2018), vol. 10704 of Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series , p. 107042S

    2018

  18. [26]

    Klein Wolt, A

    M. Klein Wolt, A. Aminaei, P. Zarka, J.-R. Schrader, A.-J. Boonstra, and H. Falcke, Planetary and Space Science 74, 167 (2012), 1209.3033

    2012 arXiv

  19. [27]

    Burns, G

    J. Burns, G. Hallinan, T.-C. Chang, M. Anderson, J. Bowman, R. Bradley, S. Furlanetto, A. Hegedus, J. Kasper, J. Kocz, et al., arXiv e-prints arXiv:2103.08623 (2021), 2103.08623

    2021 arXiv

  20. [28]

    R. S. Polidan, J. O. Burns, A. Ignatiev, A. Hegedus, J. Pober, N. Mahesh, T.-C. Chang, G. Hallinan, Y. Ning, and J. Bowman, Advances in Space Research 74, 528 (2024), 2404.03840

    2024 arXiv

  21. [29]

    T. B. H. Kuiper, D. L. Jones, M. J. Mahoney, and R. A. Preston, in Astrophysics from the Moon, edited by M. J. Mumma and H. J. Smith (AIP, 1990), vol. 207 of American Institute of Physics Conference Series, pp. 522–527. 9

    1990

  22. [30]

    X. Chen, F. Gao, F. Wu, Y. Zhang, T. Wang, W. Liu, D. Zou, F. Deng, Y. Gong, K. He, et al., Philosophical Transactions of the Royal Society of London Series A 382, 20230094 (2024), 2403.16409

    2024 arXiv

  23. [31]

    Iguchi, T

    S. Iguchi, T. Yamada, Y. Yamasaki, T. Onishi, D. Yamauchi, F. Tsuchiya, K. Takahashi, T. Matsumoto, N. Isobe, T. Iwata, et al., in Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave, edited by L. E. Coyle, S. Mat- suura, and M. D. Perrin (2024), ...

    2024

  24. [32]

    R. K. Sheth and G. Tormen, Monthly Notices of the Royal Astronomical Society 329, 61 (2002), astro-ph/0105113

    2002 arXiv

  25. [33]

    J. S. B. Wyithe and A. Loeb, The Astrophysical Journal 595, 614 (2003), astro-ph/0304156

    2003 arXiv

  26. [34]

    Matsuoka, M

    Y. Matsuoka, M. A. Strauss, N. Kashikawa, M. Onoue, K. Iwa- sawa, J.-J. Tang, C.-H. Lee, M. Imanishi, T. Nagao, M. Akiyama, et al., The Astrophysical Journal 869, 150 (2018), 1811.01963

    2018 arXiv

  27. [35]

    Matsuoka, M

    Y. Matsuoka, M. Onoue, K. Iwasawa, M. A. Strauss, N. Kashikawa, T. Izumi, T. Nagao, M. Imanishi, M. Akiyama, J. D. Silverman, et al., Astrophysical Journal Letters 949, L42 (2023), 2305.11225

    2023 arXiv

  28. [36]

    Stern, S

    D. Stern, S. G. Djorgovski, R. A. Perley, R. R. de Carvalho, and J. V. Wall, The Astronomical Journal 119, 1526 (2000), astro-ph/0001394

    2000 arXiv

  29. [37]

    Ba ˜nados, B

    E. Ba ˜nados, B. P. Venemans, E. Morganson, J. Hodge, R. De- carli, F. Walter, D. Stern, E. Schlafly, E. P. Farina, J. Greiner, et al., The Astrophysical Journal 804, 118 (2015), 1503.04214

    2015 arXiv

  30. [38]

    Y. Liu, R. Wang, E. Momjian, E. Ba ˜nados, G. Zeimann, C. J. Willott, Y. Matsuoka, A. Omont, Y. Shao, Q. Li, et al., The Astrophysical Journal 908, 124 (2021), 2012.07301

    2021 arXiv

  31. [39]

    L. G. C. Bariuan, B. Snios, M. Sobolewska, A. Siemiginowska, and D. A. Schwartz, Monthly Notices of the Royal Astronomical Society 513, 4673 (2022), 2201.04666

    2022 arXiv

  32. [40]

    Fanciullo, V

    L. Fanciullo, V. Guillet, F. Boulanger, and A. P. Jones, Astron- omy and Astrophysics 602, A7 (2017), 1702.08356

    2017 arXiv

  33. [41]

    H. L. Guo, B. Q. Chen, H. B. Yuan, Y. Huang, D. Z. Liu, Y. Yang, X. Y. Li, W. X. Sun, and X. W. Liu, The Astrophysical Journal 906, 47 (2021), 2010.14092

    2021 arXiv

  34. [42]

    W. Li, K. Inayoshi, M. Onoue, and D. Toyouchi, The Astrophys- ical Journal 950, 85 (2023), 2210.02308

    2023 arXiv

  35. [43]

    Y. Ueda, K. Hiroi, N. Isobe, M. Hayashida, S. Eguchi, M. Su- gizaki, N. Kawai, H. Tsunemi, T. Mihara, M. Matsuoka, et al., Publications of the Astronomical Society of Japan 63, S937 (2011), 1109.0852

    2011 arXiv

  36. [44]

    Y. Ueda, M. Akiyama, G. Hasinger, T. Miyaji, and M. G. Watson, The Astrophysical Journal 786, 104 (2014), 1402.1836

    2014 arXiv

  37. [45]

    Duras, A

    F. Duras, A. Bongiorno, F. Ricci, E. Piconcelli, F. Shankar, E. Lusso, S. Bianchi, F. Fiore, R. Maiolino, A. Marconi, et al., Astronomy and Astrophysics 636, A73 (2020), 2001.09984

    2020 arXiv

  38. [46]

    Square Kilometer Array Cosmology Science Working Group, D. J. Bacon, R. A. Battye, P. Bull, S. Camera, P. G. Ferreira, I. Harrison, D. Parkinson, A. Pourtsidou, M. G. Santos, et al., Publications Astronomical Society of Australia37, e007 (2020), 1811.02743

    2020 arXiv

  39. [47]

    Jeong, C

    W.-S. Jeong, C. P. Pearson, H. M. Lee, S. Pak, and T. Nakagawa, Monthly Notices of the Royal Astronomical Society 369, 281 (2006), astro-ph/0603163

    2006

  40. [48]

    Cohen, Tech

    A. Cohen, Tech. Rep. Memo #17, Long Wavelength Array (2004)

    2004

  41. [49]

    D. W. Hogg, The Astronomical Journal121, 1207 (2001), astro- ph/0004054

    2001

  42. [50]

    Tarenghi, Astrophysics and Space Science 313, 1 (2008), astro-ph/0606376

    M. Tarenghi, Astrophysics and Space Science 313, 1 (2008), astro-ph/0606376

    2008 arXiv

  43. [51]

    B. Wang, X. Zhu, C. Gao, Y. Bai, J. W. Dong, and L. J. Wang, Scientific Reports 5, 13851 (2015), 1504.05633

    2015 arXiv

  44. [52]

    An, Science China Physics, Mechanics, and Astronomy 62, 989531 (2019), 1901.07756

    T. An, Science China Physics, Mechanics, and Astronomy 62, 989531 (2019), 1901.07756

    2019 arXiv

  45. [53]

    S. Wang, S. Prunet, S. Mignot, and A. Ferrari, Astronomy and Astrophysics 692, A61 (2024), 2406.18356

    2024 arXiv

  46. [54]

    S. Guo, Y. LU, T. An, B. Lao, Z. Xu, X. Wu, and W. Lyu, Scientia Sinica Physica, Mechanica & Astronomica53, 229504 (2023)

    2023

  47. [55]

    Huang, X.-P

    Y. Huang, X.-P. Wu, Q. Zheng, J.-H. Gu, and H. Xu, Research in Astronomy and Astrophysics 16, 36 (2016), 1602.06623

    2016 arXiv

  48. [56]

    https://www.skao.int/en/explore/ telescopes/SKA-Low

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.