The Citing articles tool gives a list of articles citing the current article. The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
This article has been cited by the following article(s):
Estimating energy levels from lattice QCD correlation functions using a transfer matrix formalism
Debsubhra Chakraborty, Dhruv Sood, Archana Radhakrishnan and Nilmani Mathur Physical Review D 112(7) (2025) https://doi.org/10.1103/x3kb-8zw8
Optimal neural summarization for full-field weak lensing cosmological implicit inference
Denise Lanzieri, Justine Zeghal, T. Lucas Makinen, Alexandre Boucaud, Jean-Luc Starck and François Lanusse Astronomy & Astrophysics 697 A162 (2025) https://doi.org/10.1051/0004-6361/202451535
AKRA 2.0: Accurate Kappa Reconstruction Algorithm for masked shear catalog
Yuan Shi, Pengjie Zhang, Furen Deng, Shuren Zhou, Hongbo Cai, Ji Yao and Zeyang Sun Journal of Cosmology and Astroparticle Physics 2025(07) 038 (2025) https://doi.org/10.1088/1475-7516/2025/07/038
Hydrogen intensity mapping with MeerKAT: Preserving cosmological signal by optimising contaminant separation
I. P. Carucci, J. L. Bernal, S. Cunnington, M. G. Santos, J. Wang, J. Fonseca, K. Grainge, M. O. Irfan, Y. Li, A. Pourtsidou, M. Spinelli and L. Wolz Astronomy & Astrophysics 703 A222 (2025) https://doi.org/10.1051/0004-6361/202453461
Simulation-based inference benchmark for weak lensing cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse, Alexandre Boucaud, Gilles Louppe, Eric Aubourg and Adrian E. Bayer Astronomy & Astrophysics 699 A327 (2025) https://doi.org/10.1051/0004-6361/202452410
Hybrid summary statistics: neural weak lensing inference beyond the power spectrum
T. Lucas Makinen, Alan Heavens, Natalia Porqueres, Tom Charnock, Axel Lapel and Benjamin D. Wandelt Journal of Cosmology and Astroparticle Physics 2025(01) 095 (2025) https://doi.org/10.1088/1475-7516/2025/01/095
Generative modelling of convergence maps based on predicted one-point statistics
Forecasting the power of higher order weak-lensing statistics with automatically differentiable simulations
Denise Lanzieri, François Lanusse, Chirag Modi, Benjamin Horowitz, Joachim Harnois-Déraps and Jean-Luc Starck Astronomy & Astrophysics 679 A61 (2023) https://doi.org/10.1051/0004-6361/202346888
KaRMMa – kappa reconstruction for mass mapping
Pier Fiedorowicz, Eduardo Rozo, Supranta S Boruah, Chihway Chang and Marco Gatti Monthly Notices of the Royal Astronomical Society 512(1) 73 (2022) https://doi.org/10.1093/mnras/stac468
D. Munshi, R. Takahashi, J.D. McEwen, T.D. Kitching and F.R. Bouchet Journal of Cosmology and Astroparticle Physics 2022(05) 006 (2022) https://doi.org/10.1088/1475-7516/2022/05/006
Lifting weak lensing degeneracies with a field-based likelihood
Natalia Porqueres, Alan Heavens, Daniel Mortlock and Guilhem Lavaux Monthly Notices of the Royal Astronomical Society 509(3) 3194 (2021) https://doi.org/10.1093/mnras/stab3234
Bayesian forward modelling of cosmic shear data
Natalia Porqueres, Alan Heavens, Daniel Mortlock and Guilhem Lavaux Monthly Notices of the Royal Astronomical Society 502(2) 3035 (2021) https://doi.org/10.1093/mnras/stab204
Weak-lensing mass reconstruction using sparsity and a Gaussian random field
2D-FFTLog: efficient computation of real-space covariance matrices for galaxy clustering and weak lensing
Xiao Fang (方啸), Tim Eifler and Elisabeth Krause Monthly Notices of the Royal Astronomical Society 497(3) 2699 (2020) https://doi.org/10.1093/mnras/staa1726
Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases
Chien-Hao Lin, Joachim Harnois-Déraps, Tim Eifler, et al. Monthly Notices of the Royal Astronomical Society 499(2) 2977 (2020) https://doi.org/10.1093/mnras/staa2948
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Niall Jeffrey, Justin Alsing and François Lanusse Monthly Notices of the Royal Astronomical Society 501(1) 954 (2020) https://doi.org/10.1093/mnras/staa3594
Parameter inference and model comparison using theoretical predictions from noisy simulations
Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations
M A Price, J D McEwen, X Cai and T D Kitching (for the LSST Dark Energy Science Collaboration) Monthly Notices of the Royal Astronomical Society 489(3) 3236 (2019) https://doi.org/10.1093/mnras/stz2373
The impact of baryonic physics and massive neutrinos on weak lensing peak statistics
Ian G McCarthy, Lindsay J King, Rachel Bowyer, et al. Monthly Notices of the Royal Astronomical Society 488(3) 3340 (2019) https://doi.org/10.1093/mnras/stz1882
Distinguishing standard and modified gravity cosmologies with machine learning
On the dissection of degenerate cosmologies with machine learning
Julian Merten, Carlo Giocoli, Marco Baldi, et al. Monthly Notices of the Royal Astronomical Society 487(1) 104 (2019) https://doi.org/10.1093/mnras/stz972
On the effect of projections on convergence peak counts and Minkowski functionals