Pith. sign in

REVIEW 4 major objections 5 minor 64 references

Prospects for radio weak lensing: studies using LOFAR observations in the ELAIS-N1 field

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read The paper's central claim: with 3,200 hours of ILT observation, radio cosmic shear could be detected at about 6.8 sigma, provided telescope-blur systematics are controlled.

desk verdict Honest and useful observational study: solid HSC shear detection and a real ILT radio-optical shape correlation, but the 6.8σ forecast rests on an uncalibrated mock source density that is likely too optimistic by a factor of several. read the letter →

arxiv 2506.20845 v1 pith:RPJIFUXJ submitted 2025-06-25 astro-ph.CO

classification astro-ph.CO
keywords radioweaklensingcosmicshearLOFARELAIS-N1pointspreadfunctionsystematicsradio-opticalshapecorrelationInternationalTelescope
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 tries to establish that low-frequency radio observations are a realistic route to cosmic shear, using LOFAR observations in the ELAIS-N1 field as a concrete testbed. It first shows that the field works optically: HSC data alone give a cosmic shear detection at about $9\sigma$ over $6.4\,\mathrm{deg}^2$. It then demonstrates that high-resolution ILT radio shapes are not hopeless, measuring a radio-optical position-angle correlation of $R_{\cos(2\alpha)}=0.15\pm0.02$, which the paper reads as evidence that LOFAR can resolve the extended, star-forming galaxies whose orientations carry lensing information. On that basis it forecasts that 3200 hours of ILT observation over the same $6.7\,\mathrm{deg}^2$ pointing would detect the shear correlation at about $6.8\sigma$, provided statistical errors dominate and PSF systematics are controlled. The reason to care is that this is a quantitative, near-term path to radio-band weak lensing, with the main uncertainty stated explicitly.

What carries the argument

The argument is carried by two quantities. The first is the position-angle correlation $R_{\cos(2\alpha)}$ between matched radio and optical sources, a spin-2 correlation that measures whether radio and optical orientations track each other; the paper reports $R_{\cos(2\alpha)}=0.15\pm0.02$ and uses it as the empirical anchor that ILT shapes are physically meaningful despite PSF contamination. The second is the shear two-point correlation function $\xi_+(\theta)$, whose forecast uses a shot-noise model $\sigma_{\xi_+}=\sqrt{2}\,e_{\mathrm{rms}}^2/\sqrt{N_{\mathrm{pair}}(\theta)}$ applied to mock catalogues built from a radio continuum simulation, with the theory curve supplied by a standard lensing code. The PSF is the central obstacle: the paper shows ILT source ellipticities align with the PSF ellipse and position-angle distributions align with the PSF, so the forecast stands or falls on whether that contamination can be removed.

What would settle it

Re-analyse the existing 32-hour ILT ELAIS-N1 pointing with a shape estimator that does not rely on Gaussian fitting (for example, a visibility-domain method), then re-measure the average source ellipticity and the radio-optical correlation $R_{\cos(2\alpha)}$; a specific falsifier is if the source ellipticity stops tracking the PSF ellipse but $R_{\cos(2\alpha)}$ also drops to zero, because the forecast requires both that PSF contamination is removable and that a real shape correlation remains.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is a set of quantitative steps toward radio weak lensing in one field. First, the HSC optical data alone yield a cosmic shear detection at $9\sigma$ over $6.4\,\mathrm{deg}^2$, with a clear redshift trend. Second, matching the deep but low-resolution LoTSS radio catalogue to HSC shapes shows that the shear amplitude could be measured at about $2\sigma$ if accurate radio shapes existed. Third, cross-matching HSC with the $0.3''$ ILT sub-arcsecond sample gives a position-angle correlation of $R_{\cos(2\alpha)}=0.15\pm0.02$ between radio and optical orientations, which the paper interprets as evidence that high-resolution LOFAR can resolve the extended star-forming galaxies whose shapes matter for lensing. Fourth, using simulated radio catalogues, the paper forecasts that a 3200-hour ILT observation of the same $6.7\,\mathrm{deg}^2$ pointing would reach a source density of $6.5\,\mathrm{arcmin}^{-2}$ and detect the shear correlation at about $6.8\sigma$, under the assumption that statistical errors dominate over systematics.

Load-bearing premise

The forecast's load-bearing premise is that the telescope's point-spread-function distortion can be removed well enough that statistical noise dominates the measurement, yet the paper's own ILT data show current shapes are heavily aligned with that distortion and no method is demonstrated to remove it.

Editorial extensions

If this is right

  • A 3200-hour ILT observation of the same 6.7 deg² field would reach a usable source density of 6.5 arcmin⁻² and detect the shear correlation at about 6.8 sigma, if shot noise dominates.
  • A 128-hour version of the same observation would reach only about 1.8 sigma, so depth is the main limiting factor for ILT weak lensing.
  • The HSC-only analysis already detects cosmic shear at about 9 sigma over 6.4 deg², and the signal grows with redshift across the three tomographic bins.
  • The LoTSS-matched HSC sample shows that a 2-sigma shear amplitude measurement is possible with deep 6-arcsec-resolution radio data once accurate shapes are available.
  • The main obstacle identified is PSF systematics: current ILT shapes are heavily PSF-contaminated, so precise radio shear calibration and PSF mitigation are prerequisites for the forecast.
  • pith_inferences

Reading between the lines

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

  • If the PSF mitigation succeeds, a single ultra-deep ILT pointing could serve as a pilot for SKA-era radio weak lensing, testing the method on real data years before large radio surveys begin.
  • The measured $R_{\cos(2\alpha)}=0.15$ correlation is based on position angles only, so it is a lower bound on shape fidelity; a full ellipticity-based calibration would need to preserve this alignment after PSF correction.
  • A cheap empirical check: re-analyze the existing 32-hour ILT pointing with a visibility-domain shape estimator; if the PSF alignment disappears, the systematics may be more tractable than the current Gaussian-fitting results suggest.
  • Because the forecast scales with source density, an intermediate-depth observation between 128 and 3200 hours could bracket the true systematics floor and tell whether the 6.8-sigma target is realistic.
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

4 major / 5 minor

Summary. The paper analyzes radio and optical galaxy shapes in the ELAIS-N1 field to assess the prospects for radio weak lensing with LOFAR. Using HSC deep-layer data alone, it reports a ~9σ detection of cosmic shear over 6.4 deg^2. Using LoTSS-matched HSC sources, it measures the shear correlation amplitude at ~2σ significance. For the 0.3" ILT data, it measures a positive radio-optical position-angle correlation R_cos(2α)=0.15±0.02 and argues that ILT can resolve extended star-forming galaxies. Finally, using T-RECS mocks and PyCCL, the paper forecasts that 3200h of ILT observations could detect cosmic shear at 6.8σ, under the explicitly stated assumption that statistical errors dominate over systematics.

Significance. If the results hold, the paper would demonstrate that a single deep ILT pointing could in principle yield a cosmic shear detection at moderate significance, and that sub-arcsecond LOFAR imaging can resolve the extended radio emission needed for shape measurements. The R_cos measurement is a useful empirical anchor for future radio weak lensing work. The paper is transparent about the optimistic nature of the forecast, but the quantitative significance rests on unvalidated mock source densities and on an untested assumption about PSF mitigation; the HSC significance also appears overstated because the pivot scale is fitted to the same data. These issues are fixable with additional analysis, and the paper is a credible contribution once they are addressed.

major comments (4)
  1. [Section 6, Eq. (16)] The forecast source densities are not validated against the actual ILT observations. The mock selection uses a total-flux detection threshold (85 µJy for 128h and 17 µJy for 3200h), whereas the real 32h catalogue is detected by PyBDSF on the basis of peak surface brightness at S/N>5 (Table 1), yielding only 0.30 arcmin^-2 after the size and morphology cuts (Table 4). Since the S/N of the shear correlation scales as the square root of the source density, an overestimate of n_g by a factor f would reduce the quoted 6.8σ to 6.8/sqrt(f). The authors should apply the same peak-flux-based selection to the T-RECS mocks or calibrate the mock number counts against the existing 32h catalogue before quoting forecast significances.
  2. [Sections 5.2, 6, and Appendix B] The forecast assumes that statistical errors dominate over systematics, but the paper's own ILT data show strong PSF contamination: the average source ellipticity matches the PSF ellipse (Section 5.2) and the position-angle distribution aligns with the PSF (Figure B.3). The 6.8σ forecast therefore requires a PSF-mitigation step that is neither demonstrated nor simulated. The authors should either present an image-level simulation showing that the residual PSF leakage after a proposed calibration is below the shot-noise level, or recast the 6.8σ as an upper limit supported by a quantitative systematic-error budget.
  3. [Section 4, Eq. (14), Table 3] The ~9σ significance quoted in the abstract is based on amplitudes evaluated at a pivot scale theta_p=3.2' that was fitted to the same data, with the slope gamma fixed at 0.95; the errors in Table 3 do not include the uncertainty in theta_p and gamma. The significance should be recomputed from the full covariance of the measured xi+ points, or the amplitude errors should be marginalized over theta_p and gamma before the 9σ detection is presented as the headline result.
  4. [Section 6, Eq. (16)] The shot-noise formula in Eq. (16) adopts e_rms=0.3 per component and omits measurement noise, even though Table 4 lists the ILT intrinsic dispersion as 0.365 and the measurement noise as 0.227 per component at 32h. The authors should justify the adopted e_rms and include measurement noise, or demonstrate that it becomes negligible for a 3200h observation; they should also propagate the uncertainty in the fitted n(z) parameters and consider sample variance over the 6.7 deg^2 footprint, rather than only pair-count shot noise.
minor comments (5)
  1. [Table 2] The last row of Table 2 repeats the redshift range '1.1<z<=2.0' instead of '0.1<z<=2.0' for the combined sample.
  2. [Table 4] The HSC x ILT sub-arcsecond row contains a stray arrow and a 'Radio shape' label that breaks the row structure; the table would be clearer if the radio and optical shape columns were properly aligned.
  3. [Sections 3.4 and 4] The paper relies on 'Bisigello et al. (in prep.)' for the 99% matching fraction and the radio redshift distribution; if that work is not public, the authors should either provide the relevant numbers in the text or cite a public version.
  4. [Section 6] The rationale for using a 10-sigma total-flux detection threshold in the mocks rather than the 5-sigma peak-S/N threshold used in the real catalogues should be stated, since this choice directly affects the forecast source densities.
  5. [Figure 5] The caption reports chi^2 = 13.0 without stating the number of degrees of freedom; please include it so the quality of the power-law fit can be assessed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the shear measurements are empirical and externally calibrated, while the ILT forecast is an explicitly forward sensitivity calculation.

full rationale

None of the paper's load-bearing results reduces to its own inputs by construction. The HSC 9-sigma cosmic shear detection uses the published HSC Y1 shear calibration simulations (Mandelbaum et al. 2018a) and is compared with an independent PyCCL theory prediction; the amplitude is fit to the data but is not called a prediction. The LoTSS-matched 2-sigma result uses HSC shapes for radio-selected sources, not radio shapes, so no fitted radio parameter is being relabeled as a detection. The ILT position-angle correlation R_cos(2alpha)=0.15+/-0.02 is a direct empirical statistic, and while the paper acknowledges strong PSF contamination, that is a systematic-error limitation rather than a circular derivation. The 3200-hour forecast is a forward sensitivity calculation: T-RECS provides the mock source density and redshift distribution, PyCCL provides the theoretical xi_+, and Eq. (16) computes the shot-noise error from the mock pair counts. The predicted significance is therefore exactly the assumed input signal divided by the assumed noise, which is how a forecast is meant to work; it is not presented as a measurement. The paper's own caveats--"we do not correct the ILT measured shapes", "forecasts are optimistic, as they do not account for measurement noise or systematic errors", and the discussion of PSF and deconvolution challenges--are explicitly stated limitations. The skeptic's concern that the mock total-flux selection may overpredict usable source density relative to PyBDSF peak-surface-brightness detection is an internal-consistency and correctness risk for the forecast, but it does not make the forecast circular: the forecast would be wrong, not self-referential. No load-bearing argument depends on a self-citation chain or on a uniqueness theorem from the authors.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the standard weak lensing formalism, the transferability of HSC Y1 shear calibration to a Deep-layer field, the fidelity of T-RECS mock catalogs, and the assumption that future deep ILT observations can be made systematics-limited. The power-law fit introduces two fitted nuisance parameters (theta_p, gamma), and the forecast introduces an assumed shape dispersion and fitted n(z) parameters.

free parameters (5)
  • theta_p (pivot scale) = 3.2 arcmin
    Chosen to minimize covariance between amplitude A and slope gamma in the power-law fit to the HSC shear correlation function (Section 4).
  • gamma (power-law slope) = 0.95
    Fitted to the two higher-redshift HSC tomographic bins, then fixed for all bins when measuring amplitudes (Section 4).
  • e_rms (intrinsic shape dispersion in forecast) = 0.3 per component
    Assumed for the ILT forecasts in Section 6 to convert pair counts into shear correlation noise; not measured from the mock or real ILT data.
  • n(z) model parameters (A, a, b, c) for mocks = 128h: 0.457,0.441,3.923,0.637; 3200h: 0.093,0.410,3.014,0.352
    Fitted to T-RECS mock redshift distributions using the Fu et al. (2008) parametrization (Table 5).
  • alpha (PSF leakage) = 0.021±0.007
    Estimated from galaxy-PSF cross-correlation in Appendix A; used to assess the HSC PSF systematic.
assumptions (5)
  • standard math Standard weak lensing theory: lens equation, shear two-point correlation function, and Limber projection.
    Section 2 provides the framework for interpreting galaxy ellipticities as shear estimators.
  • domain assumption HSC Y1 shear calibration code remains valid for the ELAIS-N1 Deep layer sample.
    Section 3.3 argues that S/N and seeing distributions resemble the Y1 Wide fields, but no direct validation against external shear calibrations is presented.
  • domain assumption T-RECS simulations accurately represent radio source counts, sizes, and redshift distributions for deep LOFAR forecasts.
    Section 6 uses T-RECS mocks to set source densities and n(z) for the 128-hour and 3200-hour forecasts.
  • ad hoc to paper Statistical errors dominate over systematics in the forecast.
    Section 6 explicitly assumes statistical errors dominate; the paper's own data show PSF contamination is currently significant, making this the fragile premise for the 6.8 sigma claim.
  • domain assumption PyBDSF deconvolved shapes from 0.3 arcsecond ILT images reliably trace source morphology after simple morphology and size cuts.
    Section 5.2 and Appendix C use PyBDSF position angles and compare resolutions, but do not calibrate against known shear inputs.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Prospects for radio weak lensing: studies using LOFAR observations in the ELAIS-N1 field." pith.science (2026). https://pith.science/paper/RPJIFUXJ

@misc{pith2026250620845,
  author       = {Pith},
  title        = {Pith review of: Prospects for radio weak lensing: studies using LOFAR observations in the ELAIS-N1 field},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RPJIFUXJ}},
  note         = {Machine review of arXiv:2506.20845}
}
abstract

We carry out a shape and weak lensing analysis of Low Frequency Array (LOFAR) radio sources and Hyper Suprime-Cam (HSC) optical sources within the European Large Area Infrared Space Observatory Survey-North 1 (ELAIS-N1) field. Using HSC data alone, we detect a cosmic shear correlation signal at a significance of $\sim$$9\sigma$ over a $\sim$$6.4$ deg$^2$ region. For the radio dataset, we analyse observations from both the LOFAR Two Metre Sky Survey (LoTSS) and the International LOFAR Telescope (ILT). While LoTSS provides the deepest radio imaging of ELAIS-N1 with a central source density of $\sim$2.7 arcmin$^{-2}$, its $6^{\prime\prime}$ resolution limits the accuracy of shape measurements. But, using LoTSS-matched HSC sources, we show that accurate radio shape measurements would enable us to measure the amplitude of the shear correlation function at least at $\sim$2$\sigma$ significance. In contrast, ILT observation of the field offers a superior $0.3^{\prime\prime}$ resolution. By cross-matching HSC and ILT samples, we measure a position angle correlation of $R_{\cos(2\alpha)} = 0.15 \pm 0.02$. This result highlights ILT's ability to resolve extended and diffuse emission. The current ILT observations lack the required depth for robust weak lensing measurements. To assess the potential of ILT, we use simulated data with increased observation hours. Our analysis indicates that with 3200 hours of ILT observations or deeper data, and assuming that statistical errors dominate over systematics, a shear correlation could be detected with moderate significance. To achieve this will require precise radio shear measurements and effective mitigation of point spread function (PSF) systematics.

Figures

Figures reproduced from arXiv: 2506.20845 by the authors.

Figure 1
Figure 1. Sky coverage of ELAIS-N1 data from LoTSS Deep Fields DR2 (∼24.5 deg2 ), ILT sub-arcsecond observation (∼6.7 deg2 ), and HSC Deep Fields (∼6.4 deg2 ). The holes in HSC coverage are bright star masks. 3. Data The European Large Area Infrared Space Observatory-North 1 (ELAIS-N1) field was originally chosen as one of the regions in the Northern Hemisphere for the ELAIS survey (Oliver et al. 2000) and has since been cove… view at source ↗
Figure 2
Figure 2. Unweighted distributions of the i-band CModel S/N and seeing FWHM for the galaxies in the HSC ELAIS-N1 weak lensing sample and in the six disjointed fields of the HSC Y1 shear catalogue (XMM, GAMA15H, GAMA09H, HECTOMAP, VVDS, and WIDE12H). The seeing FWHM is calculated assuming a Gaussian PSF. The final HSC ELAIS-N1 weak lensing sample contains 456 593 sources and covers a total area of 6.4 deg2 , reaching a 5σ poin… view at source ↗
Figure 3
Figure 3. Unweighted source density distribution of the weak lensing sources in the ELAIS-N1 field. The unweighted mean source density for all the sources that pass the galaxy cuts in Sect. 3.3 is 19.7 arcmin−2 . This plot was generated with a HEALPix pixelisation parameter of Nside = 4096. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 z 0.00 0.25 0.50 0.75 1.00 P(z) 0.1 < zB ≤ 0.6 0.6 < zB ≤ 1.1 1.1 < zB ≤ 2.0 LoTSS HSC [PITH_FULL_IMAGE:figu… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Photometric redshift distribution. The solid lines represent the weighted redshift PDFs for the three tomographic redshift bins: 0.1 < zB ≤ 0.6, 0.6 < zB ≤ 1.1, and 1.1 < zB ≤ 2.0 from the HSC weak lensing catalogue. The light-grey histogram shows the weighted dis￾trib…
Figure 5
Figure 5. Figure 5: Tomographic cosmic shear 2pCFs (red) with the power law fitting lines (black) using a slope of γ = 0.95. The error bars are calculated from bootstrap resampling using TreeCorr. Measurements in the grey region (θ > 30′ ) are excluded from the fitting due to the increasi…
Figure 6
Figure 6. Figure 6: Position angle distributions for all ILT sources (top), flux-binned sub-samples (middle), and size-binned sub-samples (bottom). While PSF contamination is present across all samples, its strength shows a clear dependence on source size – decreasing systematically with …
Figure 7
Figure 7. Figure 7: 2D histogram of the radio and optical position angles of the cross-matched catalogue between ILT sub-arcsecond and HSC. tion remains evident. For instance, in ILT data, we find average ellipticity values of ⟨e1⟩ = −0.053 and ⟨e2⟩ = 0.09, which match the PSF ellipse sha…
Figure 9
Figure 9. Figure 9: 21′′ × 21′′ cutout images for six sources in the cross-matched HSC, LoTSS Deep Field DR2 and ILT sub-arcsecond catalogues of ELAIS-N1. The LoTSS Deep Field DR2 images are restored with a cir￾cular Gaussian beam of FWHM 6′′, and the ILT sub-arcsecond images are restored…
Figure 10
Figure 10. Figure 10: Redshift distributions of 128-hour (green) and 3 200-hour (red) mocks. Solid lines are best-fit redshift distribution model using the n(z) parametrisation from Fu et al. (2008). The redshift bin size is 0.15. The median redshifts for 128- and 3 200-hour simulations ar…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 44 canonical work pages

  1. [1]

    Abbott, T. M. C., Abdalla, F. B., Alarcon, A., et al. 2018, Phys. Rev. D, 98, 043526

  2. [2]

    2019, PASJ, 71, 114

    Aihara, H., AlSayyad, Y ., Ando, M., et al. 2019, PASJ, 71, 114

  3. [3]

    2018, Living Reviews in Rel- ativity, 21, 2 Astropy Collaboration, Price-Whelan, A

    Amendola, L., Appleby, S., Avgoustidis, A., et al. 2018, Living Reviews in Rel- ativity, 21, 2 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33

  4. [4]

    & Schneider, P

    Bartelmann, M. & Schneider, P. 2001, Phys. Rep., 340, 291

  5. [5]

    A., Brown, M

    Battye, R. A., Brown, M. L., Casey, C. M., et al. 2020, MNRAS, 495, 1706

  6. [6]

    H., White, R

    Becker, R. H., White, R. L., & Helfand, D. J. 1995, ApJ, 450, 559

  7. [7]

    Bernstein, G. M. & Jarvis, M. 2002, AJ, 123, 583

  8. [8]

    & Arnouts, S

    Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393

Show all 64 references
  1. [9]

    N., Kondapally, R., Williams, W

    Best, P. N., Kondapally, R., Williams, W. L., et al. 2023, MNRAS, 523, 1729

  2. [10]

    2018, Monthly Notices of the Royal Astronomical Society, 482, 2

    Bonaldi, A., Bonato, M., Galluzzi, V ., et al. 2018, Monthly Notices of the Royal Astronomical Society, 482, 2

  3. [11]

    Bonaldi, A., Harrison, I., Camera, S., & Brown, M. L. 2016, MNRAS, 463, 3686

  4. [12]

    2018, PASJ, 70, S5

    Bosch, J., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S5

  5. [13]

    2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14), 23

    Brown, M., Bacon, D., Camera, S., et al. 2015, in Advancing Astrophysics with the Square Kilometre Array (AASKA14), 23

  6. [14]

    Brown, M. L. & Battye, R. A. 2011, MNRAS, 410, 2057

  7. [15]

    Camera, S., Harrison, I., Bonaldi, A., & Brown, M. L. 2017, MNRAS, 464, 4747

  8. [16]

    E., McEwen, J

    Carrillo, R. E., McEwen, J. D., & Wiaux, Y . 2012, MNRAS, 426, 1223

  9. [17]

    E., McEwen, J

    Carrillo, R. E., McEwen, J. D., & Wiaux, Y . 2014, MNRAS, 439, 3591

  10. [18]

    Chang, T.-C., Refregier, A., & Helfand, D. J. 2004, ApJ, 617, 794

  11. [19]

    E., Alonso, D., Krause, E., et al

    Chisari, N. E., Alonso, D., Krause, E., et al. 2019, ApJS, 242, 2

  12. [20]

    L., Ravi, V ., & Hallinan, G

    Connor, L., Bouman, K. L., Ravi, V ., & Hallinan, G. 2022, MNRAS, 514, 2614

  13. [21]

    2015, A&A, 576, A7

    Dabbech, A., Ferrari, C., Mary, D., et al. 2015, A&A, 576, A7

  14. [22]

    2023, Phys

    Dalal, R., Li, X., Nicola, A., et al. 2023, Phys. Rev. D, 108, 123519 de Jong, J. M. G. H. J., van Weeren, R. J., Sweijen, F., et al. 2024, arXiv e-prints, arXiv:2407.13247 de Jong, J. T. A., Verdoes Kleijn, G. A., Kuijken, K. H., & Valentijn, E. A. 2013, Experimental Astronom...

  15. [23]

    & Brown, M

    Demetroullas, C. & Brown, M. L. 2016, MNRAS, 456, 3100

  16. [24]

    & Brown, M

    Demetroullas, C. & Brown, M. L. 2018, MNRAS, 473, 937 Euclid Collaboration, Mellier, Y ., Abdurro’uf, et al. 2024, arXiv e-prints, arXiv:2405.13491

  17. [25]

    2014, MOC - HEALPix Multi-Order Coverage map Version 1.0, IVOA Recommendation 02 June 2014

    Fernique, P., Boch, T., Donaldson, T., et al. 2014, MOC - HEALPix Multi-Order Coverage map Version 1.0, IVOA Recommendation 02 June 2014

  18. [26]

    2008, A&A, 479, 9

    Fu, L., Semboloni, E., Hoekstra, H., et al. 2008, A&A, 479, 9

  19. [27]

    2021, MNRAS, 504, 4312

    Gatti, M., Sheldon, E., Amon, A., et al. 2021, MNRAS, 504, 4312

  20. [28]

    2021, A&A, 645, A105 Górski, K

    Giblin, B., Heymans, C., Asgari, M., et al. 2021, A&A, 645, A105 Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759

  21. [29]

    2020, PASJ, 72, 16

    Hamana, T., Shirasaki, M., Miyazaki, S., et al. 2020, PASJ, 72, 16

  22. [30]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

  23. [31]

    L., Tunbridge, B., et al

    Harrison, I., Brown, M. L., Tunbridge, B., et al. 2020, MNRAS, 495, 1737

  24. [32]

    Harrison, I., Camera, S., Zuntz, J., & Brown, M. L. 2016, MNRAS, 463, 3674

  25. [33]

    2012, MNRAS, 427, 146

    Heymans, C., Van Waerbeke, L., Miller, L., et al. 2012, MNRAS, 427, 146

  26. [34]

    2019, PASJ, 71, 43

    Hikage, C., Oguri, M., Hamana, T., et al. 2019, PASJ, 71, 43

  27. [35]

    L., Harrison, I., & Whittaker, L

    Hillier, T., Brown, M. L., Harrison, I., & Whittaker, L. 2019, MNRAS, 488, 5420

  28. [36]

    & Seljak, U

    Hirata, C. & Seljak, U. 2003, MNRAS, 343, 459 Högbom, J. A. 1974, A&AS, 15, 417

  29. [37]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90 Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

  30. [38]

    2004, MNRAS, 352, 338

    Jarvis, M., Bernstein, G., & Jain, B. 2004, MNRAS, 352, 338

  31. [39]

    R., Selig, M., & Enßlin, T

    Junklewitz, H., Bell, M. R., Selig, M., & Enßlin, T. A. 2016, A&A, 586, A76

  32. [40]

    1995, ApJ, 449, 460

    Kaiser, N., Squires, G., & Broadhurst, T. 1995, ApJ, 449, 460

  33. [41]

    2015, Reports on Progress in Physics, 78, 086901

    Kilbinger, M. 2015, Reports on Progress in Physics, 78, 086901

  34. [42]

    N., Hardcastle, M

    Kondapally, R., Best, P. N., Hardcastle, M. J., et al. 2021, A&A, 648, A3

  35. [43]

    2015, MNRAS, 454, 3500

    Kuijken, K., Heymans, C., Hildebrandt, H., et al. 2015, MNRAS, 454, 3500

  36. [44]

    2011, arXiv e-prints, arXiv:1110.3193

    Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193

  37. [45]

    Limber, D. N. 1953, ApJ, 117, 134

  38. [46]

    & Rafferty, D

    Mohan, N. & Rafferty, D. 2015, PyBDSF: Python Blob Detection and Source

  39. [47]

    K., Sweijen, F., Radcliffe, J

    Morabito, L. K., Sweijen, F., Radcliffe, J. F., et al. 2022, MNRAS, 515, 5758

  40. [48]

    M., et al

    Oliver, S., Rowan-Robinson, M., Alexander, D. M., et al. 2000, MNRAS, 316, 749 pandas development team, T. 2020, pandas-dev/pandas: Pandas

  41. [49]

    J., Beswick, R

    Patel, P., Bacon, D. J., Beswick, R. J., Muxlow, T. W. B., & Hoyle, B. 2010, MNRAS, 401, 2572

  42. [50]

    & Miller, L

    Rivi, M. & Miller, L. 2018, MNRAS, 476, 2053

  43. [51]

    Rivi, M., Miller, L., Makhathini, S., & Abdalla, F. B. 2016, MNRAS, 463, 1881

  44. [52]

    N., Tasse, C., et al

    Sabater, J., Best, P. N., Tasse, C., et al. 2021, A&A, 648, A2

  45. [53]

    W., Hale, C

    Shimwell, T. W., Hale, C. L., Best, P. N., et al. 2025, arXiv e-prints, arXiv:2501.04093

  46. [54]

    W., Hardcastle, M

    Shimwell, T. W., Hardcastle, M. J., Tasse, C., et al. 2022, A&A, 659, A1

  47. [55]

    W., Röttgering, H

    Shimwell, T. W., Röttgering, H. J. A., Best, P. N., et al. 2017, A&A, 598, A104

  48. [56]

    W., Tasse, C., Hardcastle, M

    Shimwell, T. W., Tasse, C., Hardcastle, M. J., et al. 2019, A&A, 622, A1 Square Kilometre Array Cosmology Science Working Group, Bacon, D. J., Bat- tye, R. A., et al. 2020, PASA, 37, e007

  49. [57]

    J., Röttgering, H

    Sweijen, F., van Weeren, R. J., Röttgering, H. J. A., et al. 2022, Nature Astron- omy, 6, 350

  50. [58]

    2018, PASJ, 70, S9

    Tanaka, M., Coupon, J., Hsieh, B.-C., et al. 2018, PASJ, 70, S9

  51. [59]

    J., et al

    Tasse, C., Shimwell, T., Hardcastle, M. J., et al. 2021, A&A, 648, A1 The Dark Energy Survey Collaboration. 2005, arXiv e-prints, astro

  52. [60]

    Tunbridge, B., Harrison, I., & Brown, M. L. 2016, MNRAS, 463, 3339 van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2

  53. [61]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. Stéfan van der Walt & Jarrod Millman, 56 – 61

  54. [62]

    L., Becker, R

    White, R. L., Becker, R. H., Helfand, D. J., & Gregg, M. D. 1997, ApJ, 475, 479

  55. [63]

    2019, Journal of Open Source Software, 4, 1298

    Zonca, A., Singer, L., Lenz, D., et al. 2019, Journal of Open Source Software, 4, 1298

  56. [64]

    2013, MNRAS, 434, 1604 Article number, page 12 of 14 J

    Zuntz, J., Kacprzak, T., V oigt, L., et al. 2013, MNRAS, 434, 1604 Article number, page 12 of 14 J. Liu et al.: Prospects for radio weak lensing: studies with LOFAR observations in the ELAIS-N1 field Appendix A: PSF leakage in HSC ELAIS-N1 data To estimate the PSF systematics ...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.