arxiv: 2511.12488 · v3 · submitted 2025-11-16 · 🌌 astro-ph.CO

The first AKRA mass map reconstruction from HSC Y1 data

Yuan Shi , Pengjie Zhang , Zhao Chen , Jian Qin , Li Cui , Furen Deng , Ji Yao This is my paper

Pith reviewed 2026-05-17 22:33 UTC · model grok-4.3

classification 🌌 astro-ph.CO

keywords weak lensingconvergence mapmass mappingHSC Y1shear catalogkappa reconstructioncosmology

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The pith

AKRA reconstructs unbiased convergence maps from the real HSC Y1 shear catalog.

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

The paper presents the first application of the AKRA algorithm to actual weak lensing data from the Subaru Hyper Suprime-Cam Year 1 survey. AKRA is a prior-free maximum-likelihood method built to produce convergence kappa maps while correcting for survey masks and spatially varying noise. Validation on mock shear catalogs from the Kun simulations, using masks for the six HSC Y1 fields, confirms that the lensing power spectrum, second and third moments of kappa, and the one-point probability distribution function remain unbiased. The authors then run the algorithm on the real HSC Y1 shear catalog and release the resulting kappa maps for further use. Accurate maps matter because they allow direct study of the projected matter distribution without the systematic biases that have limited earlier reconstruction techniques.

Core claim

We have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method. It has been validated for mock shear catalogs with a variety of survey masks. In this work, we present the first real-data application of the AKRA on the Subaru Hyper Suprime-Cam Year 1 (HSC Y1) data. We first validate AKRA using mock shear catalogs from the Kun simulation suite, with masks corresponding to the six HSC Y1 regions. The investigated statistics, including the lensing power spectrum, ⟨κ²⟩, ⟨κ³⟩, and the one-point probability distribution function of κ, are all unbiased. We then apply AKRA to the HSC Y1 shear catalog and provide reconstructed κ maps

What carries the argument

AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free maximum-likelihood analytical method that reconstructs unbiased convergence kappa maps from masked shear catalogs with spatially varying noise.

If this is right

Reconstructed kappa maps from HSC Y1 data are unbiased in the lensing power spectrum.
The second moment ⟨κ²⟩ and third moment ⟨κ³⟩ of the convergence field remain unbiased.
The one-point probability distribution function of kappa is recovered without bias.
The maps are ready for direct use in subsequent cosmological or astrophysical analyses.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

These kappa maps could be cross-correlated with galaxy catalogs or CMB lensing to tighten constraints on structure growth.
The same AKRA pipeline could be run on future larger datasets such as HSC Year 3 or LSST to produce higher-resolution mass maps.
Unbiased reconstruction may reduce the need for empirical calibration steps in weak-lensing cosmology pipelines.

Load-bearing premise

The Kun simulation mock catalogs with HSC Y1 masks accurately reproduce the noise, mask, and systematic properties of the real HSC Y1 shear data.

What would settle it

If the one-point PDF or higher moments of the reconstructed kappa maps from the actual HSC Y1 data deviate significantly from the distributions measured on the Kun mocks after identical processing, the unbiased performance claim would fail.

read the original abstract

Weak lensing mass-mapping from shear catalogs faces systematic challenges from survey masks and spatially varying noise. To overcome these issues and reconstruct unbiased convergence $\kappa$ maps, we have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method. It has been validated for mock shear catalogs with a variety of survey masks. In this work, we present the first real-data application of the AKRA on the Subaru Hyper Suprime-Cam Year 1 (HSC Y1) data. We first validate AKRA using mock shear catalogs from the \texttt{Kun} simulation suite, with masks corresponding to the six HSC Y1 regions (\texttt{GAMA09H}, \texttt{GAMA15H}, \texttt{HECTOMAP}, \texttt{VVDS}, \texttt{WIDE12H}, and \texttt{XMMLSS}). The investigated statistics, including the lensing power spectrum, $\langle \kappa^2\rangle$, $\langle \kappa^3\rangle$, and the one-point probability distribution function of $\kappa$, are all unbiased. We then apply AKRA to the HSC Y1 shear catalog and provide reconstructed $\kappa$ maps ready for subsequent scientific analyses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

First real-data AKRA kappa maps from HSC Y1 after unbiased mock tests with actual masks, but the mock-to-real transfer rests on an untested assumption.

read the letter

AKRA is a prior-free maximum-likelihood method for kappa reconstruction, and this paper reports its first application to real HSC Y1 shear data. They validate on Kun simulation mocks using the exact six HSC Y1 region masks, then show that the reconstructed power spectrum, second and third moments, and one-point PDF remain unbiased. After that they run the method on the actual catalog and release the maps for further use. The practical step of folding the real masks into the mock tests is a clear improvement over generic validation. Releasing the maps themselves is also useful for groups that want to run higher-order statistics or cross-correlations without rebuilding the reconstruction pipeline. The central soft spot is the jump from mock to real data. Unbiased results on the Kun mocks only carry over if those mocks faithfully reproduce the spatially varying noise, mask-induced mode coupling, and any residual systematics in the actual HSC Y1 catalog. The abstract gives no quantitative checks on that match, such as shear variance comparisons or B-mode leakage tests between mocks and data. Without those, the claim that the real maps are ready for science use rests on an assumption rather than demonstrated fidelity. The paper is aimed at weak-lensing analysts who need mass maps from HSC or similar surveys and at method developers comparing reconstruction techniques. It is solid enough on its own terms to deserve a serious referee, even though the validation section would likely need strengthening before publication.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free maximum-likelihood analytical method for reconstructing weak-lensing convergence (κ) maps from shear catalogs while addressing survey masks and spatially varying noise. It validates AKRA on mock shear catalogs from the Kun simulation suite using masks for the six HSC Y1 regions (GAMA09H, GAMA15H, HECTOMAP, VVDS, WIDE12H, XMMLSS), reporting unbiased recovery of the lensing power spectrum, ⟨κ²⟩, ⟨κ³⟩, and the one-point PDF of κ. The method is then applied to the real HSC Y1 shear catalog to produce κ maps stated to be ready for subsequent scientific analyses.

Significance. If the unbiased performance demonstrated on the masked Kun mocks transfers to real data, AKRA would provide a useful prior-free tool for mass mapping in complex survey geometries, supporting reliable extraction of higher-order statistics and cosmological constraints from HSC Y1 and future datasets. The analytical, maximum-likelihood formulation and the release of reconstructed maps constitute practical strengths, provided the mock-to-real transfer is rigorously justified.

major comments (2)

[Abstract] Abstract: The central claim that the reconstructed κ maps from the real HSC Y1 catalog are unbiased and ready for scientific analyses rests on the transfer of performance from Kun mocks with HSC Y1 masks; however, the manuscript provides no quantitative fidelity tests (e.g., comparison of shear variance, B-mode leakage, or spatially varying noise properties) between the mocks and the actual HSC Y1 catalog, leaving the transfer assumption unverified.
[Method] Method description: No derivation of the AKRA maximum-likelihood estimator, explicit error propagation, or quantitative benchmark against existing mass-mapping techniques (such as Kaiser-Squires or Wiener filtering) is supplied, which is required to substantiate that the reported unbiased statistics on mocks arise from the method rather than from the specific simulation setup.

minor comments (2)

[Abstract] Clarify the precise definition of 'prior-free' and how the maximum-likelihood formulation avoids implicit regularization when masks induce mode coupling.
Add a brief statement on the computational implementation and any numerical stability considerations for the six HSC Y1 regions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We address each major comment point by point below, indicating the revisions we will implement to strengthen the presentation and justification of our results.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that the reconstructed κ maps from the real HSC Y1 catalog are unbiased and ready for scientific analyses rests on the transfer of performance from Kun mocks with HSC Y1 masks; however, the manuscript provides no quantitative fidelity tests (e.g., comparison of shear variance, B-mode leakage, or spatially varying noise properties) between the mocks and the actual HSC Y1 catalog, leaving the transfer assumption unverified.

Authors: We agree that explicit quantitative fidelity tests between the Kun mocks and the real HSC Y1 catalog would provide stronger support for transferring the unbiased performance to the real-data application. Although the mocks incorporate the HSC Y1 masks and are constructed to match the survey's noise and selection properties, the original manuscript did not include direct side-by-side comparisons of shear variance, B-mode leakage, or spatially varying noise. In the revised manuscript we will add these comparisons in a dedicated subsection (or appendix) to verify the fidelity of the mocks and thereby justify the application to real data. revision: yes
Referee: [Method] Method description: No derivation of the AKRA maximum-likelihood estimator, explicit error propagation, or quantitative benchmark against existing mass-mapping techniques (such as Kaiser-Squires or Wiener filtering) is supplied, which is required to substantiate that the reported unbiased statistics on mocks arise from the method rather than from the specific simulation setup.

Authors: We acknowledge that a fuller mathematical presentation of the AKRA method is warranted. The manuscript describes the algorithm at a high level but does not contain the explicit derivation of the maximum-likelihood estimator or the associated error-propagation expressions. We will add a new section providing the complete derivation from the likelihood function, including error propagation. In addition, while the unbiased recovery of power spectrum, variance, skewness, and PDF on the mocks already indicates the method's effectiveness, we agree that direct quantitative benchmarks against Kaiser-Squires and Wiener filtering are useful. We will include these comparisons on the same set of masked Kun mocks, reporting the recovered statistics for each technique. revision: yes

Circularity Check

0 steps flagged

AKRA is an analytical maximum-likelihood method validated on independent Kun mocks; derivation self-contained with no circular reductions

full rationale

The paper describes AKRA as a prior-free, maximum-likelihood analytical reconstruction method. Validation uses external Kun simulation mocks incorporating HSC Y1 masks, with unbiased results reported for power spectrum, ⟨κ²⟩, ⟨κ³⟩, and κ PDF. Application to real HSC Y1 data follows directly. No equations, self-citations, or fitted parameters are shown reducing the central claims to inputs by construction. The derivation chain relies on independent mock validation rather than self-referential fits or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that maximum-likelihood estimation yields unbiased kappa under the survey mask and noise model, plus that Kun mocks faithfully represent real HSC systematics.

axioms (1)

domain assumption Maximum-likelihood estimation under the assumed shear noise model produces unbiased convergence maps even with complex survey masks.
The method is explicitly maximum-likelihood based and prior-free.

invented entities (1)

AKRA algorithm no independent evidence
purpose: Analytical reconstruction of unbiased kappa maps from masked shear catalogs
New method constructed and named in this work.

pith-pipeline@v0.9.0 · 5525 in / 1209 out tokens · 34001 ms · 2026-05-17T22:33:26.361376+00:00 · methodology

discussion (0)

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Relation between the paper passage and the cited Recognition theorem.

We have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method... γ = Aκ + n... ˆκ = (AᵀN⁻¹A + R)⁻¹ AᵀN⁻¹γ

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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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Works this paper leans on

86 extracted references · 86 canonical work pages · 18 internal anchors

[1]

Bartelmann and P

M. Bartelmann and P. Schneider, Weak gravitational lensing, Physics Report340(2001) 291. – 10 –

work page 2001
[2]

Refregier, Weak gravitational lensing by large-scale structure, in Annual Review of Astronomy and Astrophysics, vol

A. Refregier, Weak gravitational lensing by large-scale structure, in Annual Review of Astronomy and Astrophysics, vol. 41, pp. 645–668, 2003, DOI

work page 2003
[3]

Munshi, P

D. Munshi, P. Valageas, L. van Waerbeke and A. Heavens, Cosmology with weak lensing surveys, Physics Reports462(2008) 67

work page 2008
[4]

Fu and Z.H

L.P. Fu and Z.H. Fan, Probing the dark side of the universe with weak gravitational lensing effects, Research in Astronomy and Astrophysics14(2014) 1061

work page 2014
[5]

Kilbinger, Cosmology with cosmic shear observations: A review, Reports on Progress in Physics78 (2015)

M. Kilbinger, Cosmology with cosmic shear observations: A review, Reports on Progress in Physics78 (2015)

work page 2015
[6]

Mandelbaum, Weak lensing for precision cosmology, Annual Review of Astronomy and Astrophysics56(2018) 393

R. Mandelbaum, Weak lensing for precision cosmology, Annual Review of Astronomy and Astrophysics56(2018) 393

work page 2018
[7]

Abbott, F.B

T. Abbott, F.B. Abdalla, J. Aleksi ´c, S. Allam, A. Amara, D. Bacon et al., The dark energy survey: More than dark energy - an overview, Monthly Notices of the Royal Astronomical Society460(2016) 1270

work page 2016
[8]

A. Amon, D. Gruen, M.A. Troxel, N. Maccrann, S. Dodelson, A. Choi et al., Dark energy survey year 3 results: Cosmology from cosmic shear and robustness to data calibration, Physical Review D105(2022)

work page 2022
[9]

Secco, S

L.F. Secco, S. Samuroff, E. Krause, B. Jain, J. Blazek, M. Raveri et al.,Dark energy survey year 3 results: Cosmology from cosmic shear and robustness to modeling uncertainty, Physical Review D105(2022)

work page 2022
[10]

Aihara, R

H. Aihara, R. Armstrong, S. Bickerton, J. Bosch, J. Coupon, H. Furusawa et al., First data release of the hyper suprime-cam subaru strategic program, Publications of the Astronomical Society of Japan70 (2018)

work page 2018
[11]

Hikage, M

C. Hikage, M. Oguri, T. Hamana, S. More, R. Mandelbaum, M. Takada et al., Cosmology from cosmic shear power spectra with subaru hyper suprime-cam first-year data, Publications of the Astronomical Society of Japan71(2019)

work page 2019
[12]

Hamana, M

T. Hamana, M. Shirasaki, S. Miyazaki, C. Hikage, M. Oguri, S. More et al., Cosmological constraints from cosmic shear two-point correlation functions with hsc survey first-year data, Publications of the Astronomical Society of Japan72(2020)

work page 2020
[13]

Kuijken, C

K. Kuijken, C. Heymans, H. Hildebrandt, R. Nakajima, T. Erben, J.T.A. De Jong et al., Gravitational lensing analysis of the kilo-degree survey, Monthly Notices of the Royal Astronomical Society454 (2015) 3500

work page 2015
[14]

Asgari, C.A

M. Asgari, C.A. Lin, B. Joachimi, B. Giblin, C. Heymans, H. Hildebrandt et al., Kids-1000 cosmology: Cosmic shear constraints and comparison between two point statistics, Astronomy and Astrophysics645 (2021)

work page 2021
[15]

J. Yao, H. Shan, R. Li, Y. Xu, D. Fan, D. Liu et al., Csst wl preparation i: forecast the impact from non-gaussian covariances and requirements on systematics control, Monthly Notices of the Royal Astronomical Society527(2024) 5206

work page 2024
[16]

Laureijs, J

R. Laureijs, J. Amiaux, S. Arduini, J.L. Augu ˇSres, J. Brinchmann, R. Cole et al., Euclid definition study report, Arxiv (2011)

work page 2011
[17]

Euclid, Y

C. Euclid, Y. Mellier, Abdurro’uf, J.A.A. Barroso, A. Achucarro, J. Adamek et al.,Euclid. i. overview of the euclid mission,

work page
[18]

LSST Science Book, Version 2.0

L.S. Collaboration, P.A. Abell, J. Allison, S.F. Anderson, J.R. Andrew, J.R.P. Angel et al., Lsst science book, version 2.0, December 01, 2009, 2009. 10.48550/arXiv.0912.0201

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.0912.0201 2009
[19]

Ivezi´c, S.M

ˇZ. Ivezi´c, S.M. Kahn, J.A. Tyson, B. Abel, E. Acosta, R. Allsman et al.,Lsst: From science drivers to reference design and anticipated data products, Astrophysical Journal873(2019)

work page 2019
[20]

Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report

D. Spergel, N. Gehrels, C. Baltay, D. Bennett, J. Breckinridge, M. Donahue et al., Wide-field infrarred survey telescope-astrophysics focused telescope assets wfirst-afta 2015 report, March 01, 2015, 2015. 10.48550/arXiv.1503.03757. – 11 –

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1503.03757 2015
[21]

Y. Gong, X. Liu, Y. Cao, X. Chen, Z. Fan, R. Li et al., Cosmology from the chinese space station optical survey (css-os), ASTROPHYSICALJOURNAL883(2019)

work page 2019
[22]

H. Zhan, The wide-field multiband imaging and slitless spectroscopy survey to be carried out by the survey space telescope of china manned space program, Kexue Tongbao/Chinese Science Bulletin66 (2021) 1290

work page 2021
[23]

H. Shan, X. Liu, H. Hildebrandt, C. Pan, N. Martinet, Z. Fan et al., KiDS-450: cosmological constraints from weak lensing peak statistics - I. Inference from analytical prediction of high signal-to-noise ratio convergence peaks, Monthly Notices of the Royal Astronomical Society474(2018) 1116 [1709.07651]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[24]

KiDS-450: Cosmological Constraints from Weak Lensing Peak Statistics - II: Inference from Shear Peaks using N-body Simulations

N. Martinet, P. Schneider, H. Hildebrandt, H. Shan, M. Asgari, J.P. Dietrich et al., KiDS-450: cosmological constraints from weak-lensing peak statistics - II: Inference from shear peaks using N-body simulations, Monthly Notices of the Royal Astronomical Society474(2018) 712 [1709.07678]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[25]

Liu, X.M

D.Z. Liu, X.M. Meng, X.Z. Er, Z.H. Fan, M. Kilbinger, G.L. Li et al., Potential scientific synergies in weak lensing studies between the CSST and Euclid space probes, Astronomy & Astrophysics669(2023) A128 [2210.16341]

work page arXiv 2023
[26]

CFHTLenS: Mapping the Large Scale Structure with Gravitational Lensing

L. Van Waerbeke, J. Benjamin, T. Erben, C. Heymans, H. Hildebrandt, H. Hoekstra et al., CFHTLenS: mapping the large-scale structure with gravitational lensing, Monthly Notices of the Royal Astronomical Society433(2013) 3373 [1303.1806]

work page internal anchor Pith review Pith/arXiv arXiv 2013
[27]

Emulating the CFHTLenS Weak Lensing data: Cosmological Constraints from moments and Minkowski functionals

A. Petri, J. Liu, Z. Haiman, M. May, L. Hui and J.M. Kratochvil, Emulating the CFHTLenS weak lensing data: Cosmological constraints from moments and Minkowski functionals, Physical Review D91(2015) 103511 [1503.06214]

work page internal anchor Pith review Pith/arXiv arXiv 2015
[28]

Dark Energy Survey Year 1 Results: Curved-Sky Weak Lensing Mass Map

C. Chang, A. Pujol, B. Mawdsley, D. Bacon, J. Elvin-Poole, P. Melchior et al., Dark Energy Survey Year 1 results: curved-sky weak lensing mass map, Monthly Notices of the Royal Astronomical Society475(2018) 3165 [1708.01535]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[29]

Probing Cosmology with Weak Lensing Minkowski Functionals

J.M. Kratochvil, E.A. Lim, S. Wang, Z. Haiman, M. May and K. Huffenberger, Probing cosmology with weak lensing Minkowski functionals, Physical Review D85(2012) 103513 [1109.6334]

work page internal anchor Pith review Pith/arXiv arXiv 2012
[30]

Minkowski Functionals of Convergence Maps and the Lensing Figure of Merit

M. Vicinanza, V.F. Cardone, R. Maoli, R. Scaramella, X. Er and I. Tereno, Minkowski functionals of convergence maps and the lensing figure of merit,Physical Review D99 (2019) 043534 [1905.00410]

work page internal anchor Pith review Pith/arXiv arXiv 2019
[31]

Z¨ urcher, J

D. Z¨ urcher, J. Fluri, R. Sgier, T. Kacprzak and A. Refregier,Cosmological forecast for non-gaussian statistics in large-scale weak lensing surveys, Journal of Cosmology and Astroparticle Physics2021 (2021) 028–028

work page 2021
[32]

Cheng, Y.S

S. Cheng, Y.S. Ting, B. Menard and J. Bruna, A new approach to observational cosmology using the scattering transform, MONTHLY NOTICES OF THE ROYALASTRONOMICAL SOCIETY499 (2020) 5902

work page 2020
[33]

Cheng and B

S. Cheng and B. M ´enard, Weak lensing scattering transform: dark energy and neutrino mass sensitivity, MONTHLY NOTICES OF THE ROYALASTRONOMICAL SOCIETY507(2021) 1012

work page 2021
[34]

Takada and B

M. Takada and B. Jain, Three-point correlations in weak lensing surveys: Model predictions and applications, MONTHLY NOTICES OF THE ROYALASTRONOMICAL SOCIETY344(2003) 857

work page 2003
[35]

Non-Gaussian information from weak lensing data via deep learning

A. Gupta, J.M.Z. Matilla, D. Hsu and Z. Haiman, Non-Gaussian information from weak lensing data via deep learning, Physical Review D97(2018) 103515 [1802.01212]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[36]

An improved cosmological parameter inference scheme motivated by deep learning

D. Ribli, B. ´A. Pataki and I. Csabai, An improved cosmological parameter inference scheme motivated by deep learning,Nature Astronomy 3(2019) 93 [1806.05995]. – 12 –

work page internal anchor Pith review Pith/arXiv arXiv 2019
[37]

Fluri, T

J. Fluri, T. Kacprzak, A. Lucchi, A. Schneider, A. Refregier and T. Hofmann, Full w CDM analysis of KiDS-1000 weak lensing maps using deep learning, Physical Review D105 (2022) 083518 [2201.07771]

work page arXiv 2022
[38]

T. Lu, Z. Haiman and X. Li, Cosmological constraints from HSC survey first-year data using deep learning,Monthly Notices of the Royal Astronomical Society521(2023) 2050 [2301.01354]

work page arXiv 2023
[39]

A.J. Zhou, X. Li, S. Dodelson and R. Mandelbaum, Accurate field-level weak lensing inference for precision cosmology, Phys. Rev. D110(2024) 023539

work page 2024
[40]

Zeghal, D

J. Zeghal, D. Lanzieri, F. Lanusse, A. Boucaud, G. Louppe, E. Aubourg et al., Simulation-based inference benchmark for weak lensing cosmology, Astronomy and Astrophysics699(2025) A327

work page 2025
[41]

An algorithm for the direct reconstruction of the dark matter correlation function from weak lensing and galaxy clustering

T. Baldauf, R.E. Smith, U. Seljak and R. Mandelbaum, Algorithm for the direct reconstruction of the dark matter correlation function from weak lensing and galaxy clustering, PHYSICAL REVIEW D81(2010) 063531 [0911.4973]

work page internal anchor Pith review Pith/arXiv arXiv 2010
[42]

MacCrann, J

N. MacCrann, J. Blazek, B. Jain and E. Krause, Controlling and leveraging small-scale information in tomographic galaxy-galaxy lensing,Mon. Not. Roy.Astron. Soc491(2020) 5498 [1903.07101]

work page arXiv 2020
[43]

Y. Park, E. Rozo and E. Krause, Localizing Transformations of the Galaxy-Galaxy Lensing Observable, Physical Review Letters126(2021) 021301 [2004.07504]

work page arXiv 2021
[44]

J. Prat, J. Blazek, C. S ´anchez, I. Tutusaus, S. Pandey, J. Elvin-Poole et al., Dark energy survey year 3 results: High-precision measurement and modeling of galaxy-galaxy lensing, PHYSICAL REVIEW D105(2022) 083528 [2105.13541]

work page arXiv 2022
[45]

Kaiser and G

N. Kaiser and G. Squires, Mapping the dark matter with weak gravitational lensing, Astrophysical Journal404(1993) 441

work page 1993
[46]

Vikram, C

V. Vikram, C. Chang, B. Jain, D. Bacon, A. Amara, M.R. Becker et al., Wide-field lensing mass maps from dark energy survey science verification data: Methodology and detailed analysis, Physical Review D - Particles, Fields, Gravitation and Cosmology92(2015)

work page 2015
[47]

Gatti, B

M. Gatti, B. Jain, C. Chang, M. Raveri, D. Z¨ urcher, L. Secco et al.,Dark energy survey year 3 results: Cosmology with moments of weak lensing mass maps, PHYSICAL REVIEW D106(2022)

work page 2022
[48]

Hierarchical Cosmic Shear Power Spectrum Inference

J. Alsing, A. Heavens, A.H. Jaffe, A. Kiessling, B. Wandelt and T. Hoffmann, Hierarchical cosmic shear power spectrum inference, Monthly Notices of the Royal Astronomical Society455(2016) 4452 [1505.07840]

work page internal anchor Pith review Pith/arXiv arXiv 2016
[49]

Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference

J. Alsing, A. Heavens and A.H. Jaffe, Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference, Monthly Notices of the Royal Astronomical Society466(2017) 3272 [1607.00008]

work page internal anchor Pith review Pith/arXiv arXiv 2017
[50]

Porqueres, A

N. Porqueres, A. Heavens, D. Mortlock and G. Lavaux, Lifting weak lensing degeneracies with a field-based likelihood,Monthly Notices of the Royal Astronomical Society509(2022) 3194 [2108.04825]

work page arXiv 2022
[51]

Improving Weak Lensing Mass Map Reconstructions using Gaussian and Sparsity Priors: Application to DES SV

N. Jeffrey, F.B. Abdalla, O. Lahav, F. Lanusse, J.L. Starck, A. Leonard et al., Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV, Monthly Notices of the Royal Astronomical Society479(2018) 2871 [1801.08945]

work page internal anchor Pith review Pith/arXiv arXiv 2018
[52]

GLIMPSE: Accurate 3D weak lensing reconstructions using sparsity

A. Leonard, F. Lanusse and J.-L. Starck, GLIMPSE: accurate 3D weak lensing reconstructions using sparsity, Monthly Notices of the Royal Astronomical Society440(2014) 1281 [1308.1353]

work page internal anchor Pith review Pith/arXiv arXiv 2014
[53]

Price, J.D

M.A. Price, J.D. McEwen, X. Cai, T.D. Kitching and LSST Dark Energy Science Collaboration, Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations, Monthly Notices of the Royal Astronomical Society489(2019) 3236 [1812.04018]. – 13 –

work page arXiv 2019
[54]

Fiedorowicz, E

P. Fiedorowicz, E. Rozo and S.S. Boruah, KaRMMa 2.0 – Kappa Reconstruction for Mass Mapping, arXiv e-prints (2022) arXiv:2210.12280 [2210.12280]

work page arXiv 2022
[55]

Fiedorowicz, E

P. Fiedorowicz, E. Rozo, S.S. Boruah, C. Chang and M. Gatti, KaRMMa - kappa reconstruction for mass mapping, Monthly Notices of the Royal Astronomical Society512(2022) 73 [2105.14699]

work page arXiv 2022
[56]

Weak Lensing Mass Reconstruction using Wavelets

J.L. Starck, S. Pires and A. R ´efr´egier, Weak lensing mass reconstruction using wavelets,Astronomy & Astrophysics451(2006) 1139 [astro-ph/0503373]

work page internal anchor Pith review Pith/arXiv arXiv 2006
[57]

Starck, K.E

J.L. Starck, K.E. Themelis, N. Jeffrey, A. Peel and F. Lanusse, Weak-lensing mass reconstruction using sparsity and a Gaussian random field,Astronomy & Astrophysics649(2021) A99 [2102.04127]

work page arXiv 2021
[58]

Y. Shi, P. Zhang, Z. Sun and Y. Wang, Accurate kappa reconstruction algorithm for masked shear catalog, PHYSICAL REVIEW D109(2024) 123530

work page 2024
[59]

Y. Shi, P. Zhang, F. Deng, S. Zhou, H. Cai, J. Yao et al., Akra 2.0: Accurate kappa reconstruction algorithm for masked shear catalog, JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS2025(2025) 038

work page 2025
[60]

Mandelbaum, H

R. Mandelbaum, H. Miyatake, T. Hamana, M. Oguri, M. Simet, R. Armstrong et al., The first-year shear catalog of the subaru hyper suprime-cam subaru strategic program survey, Publications of the Astronomical Society of Japan70(2018)

work page 2018
[61]

X. Liu, S. Yuan, C. Pan, T. Zhang, Q. Wang and Z. Fan,Cosmological studies from hsc-ssp tomographic weak-lensing peak abundances, Monthly Notices of the Royal Astronomical Society519(2023) 594

work page 2023
[62]

T. Lu, Z. Haiman and X. Li, Cosmological constraints from hsc survey first-year data using deep learning, Monthly Notices of the Royal Astronomical Society521(2023) 2050

work page 2023
[63]

Thiele, G.A

L. Thiele, G.A. Marques, J. Liu and M. Shirasaki, Cosmological constraints from the subaru hyper suprime-cam year 1 shear catalogue lensing convergence probability distribution function, Physical Review D108(2023)

work page 2023
[64]

Grand ´on, G.A

D. Grand ´on, G.A. Marques, L. Thiele, S. Cheng, M. Shirasaki and J. Liu, Impact of baryonic feedback on hsc-y1 weak lensing non-gaussian statistics, Physical Review D110(2024)

work page 2024
[65]

Marques, J

G.A. Marques, J. Liu, M. Shirasaki, L. Thiele, D. Grand ´on, K.M. Huffenberger et al., Cosmology from weak lensing peaks and minima with subaru hyper suprime-cam survey first-year data, Monthly Notices of the Royal Astronomical Society528(2024) 4513

work page 2024
[66]

Novaes, L

C.P. Novaes, L. Thiele, J. Armijo, S. Cheng, J.A. Cowell, G.A. Marques et al., Cosmology from hsc y1 weak lensing with combined higher-order statistics and simulation-based inference, Cosmology from HSC Y1 Weak Lensing with Combined Higher-Order Statistics and Simulation-based Inference (2024)

work page 2024
[67]

Armijo, G.A

J. Armijo, G.A. Marques, C.P. Novaes, L. Thiele, J.A. Cowell, D. Grand´on et al., Cosmological constraints using minkowski functionals from the first year data of the hyper suprime-cam, Monthly Notices of the Royal Astronomical Society537(2025) 3553

work page 2025
[68]

Cheng, G.A

S. Cheng, G.A. Marques, D. Grand ´on, L. Thiele, M. Shirasaki, B. M´enard et al., Cosmological constraints from weak lensing scattering transform using hsc y1 data, Journal of Cosmology and Astroparticle Physics2025(2025)

work page 2025
[69]

Tanaka, J

M. Tanaka, J. Coupon, B.C. Hsieh, S. Mineo, A.J. Nishizawa, J. Speagle et al., Photometric redshifts for hyper suprime-cam subaru strategic program data release 1, Publications of the Astronomical Society of Japan70(2018)

work page 2018
[70]

Hikage, M

C. Hikage, M. Oguri, T. Hamana, S. More, R. Mandelbaum, M. Takada et al., Cosmology from cosmic shear power spectra with Subaru Hyper Suprime-Cam first-year data, Publications of the Astronomical Society of Japan71(2019) 43. – 14 –

work page 2019
[71]

Hamana, M

T. Hamana, M. Shirasaki, S. Miyazaki, C. Hikage, M. Oguri, S. More et al., Cosmological constraints from cosmic shear two-point correlation functions with HSC survey first-year data, Publications of the Astronomical Society of Japan72(2020)

work page 2020
[72]

Z. Chen, Y. Yu, J. Han and Y. Jing, CSST cosmological emulator I: Matter power spectrum emulation with one percent accuracy to k = 10h Mpc-1, Science China Physics, Mechanics, and Astronomy68(2025) 289512 [2502.11160]

work page arXiv 2025
[73]

J. Han, M. Li, W. Jiang, Z. Chen, H. Wang, C. Wei et al., The Jiutian simulations for the CSST extra-galactic surveys,Science China Physics, Mechanics, and Astronomy68(2025) 109511 [2503.21368]

work page arXiv 2025
[74]

Chen and Y

Z. Chen and Y. Yu, CSST cosmological emulator II: Generalized accurate halo mass function emulation, Science China Physics, Mechanics, and Astronomy68(2025) 109513 [2506.09688]

work page arXiv 2025
[75]

S. Zhou, Z. Chen and Y. Yu, CSST Cosmological Emulator III: Hybrid Lagrangian Bias Expansion Emulation of Galaxy Clustering, arXiv e-prints (2025) arXiv:2506.04671 [2506.04671]

work page arXiv 2025
[76]

Bernardeau, L

F. Bernardeau, L. van Waerbeke and Y. Mellier, Weak lensing statistics as a probe of OMEGA and power spectrum, Astronomy and Astrophysics322(1997) 1

work page 1997
[77]

Barthelemy, S

A. Barthelemy, S. Codis and F. Bernardeau, Probability distribution function of the aperture mass field with large deviation theory, Monthly Notices of the Royal Astronomical Society503(2021) 5204

work page 2021
[78]

Boyle, C

A. Boyle, C. Uhlemann, O. Friedrich, A. Barthelemy, S. Codis, F. Bernardeau et al., Nuw CDM cosmology from the weak lensing convergence PDF, Monthly Notices of the Royal Astronomical Society505(2021) 2886

work page 2021
[79]

Barthelemy, A

A. Barthelemy, A. Halder, Z. Gong and C. Uhlemann, Making the leap. Part I. Modelling the reconstructed lensing convergence PDF from cosmic shear with survey masks and systematics, Journal of Cosmology and Astroparticle Physics2024(2024) 060

work page 2024
[80]

Taruya, M

A. Taruya, M. Takada, T. Hamana, I. Kayo and T. Futamase, Lognormal Property of Weak-Lensing Fields, The Astrophysical Journal571(2002) 638

work page 2002

Showing first 80 references.