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Home All issues Volume 679 (November 2023) A&A, 679 (2023) A61 References
Open Access
Issue
A&A
Volume 679, November 2023
Article Number A61
Number of page(s) 18
Section Cosmology (including clusters of galaxies)
DOI https://doi.org/10.1051/0004-6361/202346888
Published online 07 November 2023
  1. Ade, P. A., Aghanim, N., Arnaud, M., et al. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  2. Ajani, V., Peel, A., Pettorino, V., et al. 2020, Phys. Rev. D, 102, 103531 [Google Scholar]
  3. Ajani, V., Starck, J.-L., & Pettorino, V. 2021, A&A, 645, L11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Alsing, J., Wandelt, B., & Feeney, S. 2018, MNRAS, 477, 2874 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bernardeau, F., van Waerbeke, L., & Mellier, Y. 1997, A&A, 322, 1 [NASA ADS] [Google Scholar]
  6. Blandford, R., & Narayan, R. 1986, ApJ, 310, 568 [Google Scholar]
  7. Blazek, J. A., MacCrann, N., Troxel, M. A., & Fang, X. 2019, Phys. Rev. D, 100, 103506 [NASA ADS] [CrossRef] [Google Scholar]
  8. Böhm, V., Feng, Y., Lee, M. E., & Dai, B. 2021, Astron. Comput., 36, 100490 [CrossRef] [Google Scholar]
  9. Boruah, S. S., Rozo, E., & Fiedorowicz, P. 2022, MNRAS, 516, 4111 [NASA ADS] [CrossRef] [Google Scholar]
  10. Boyle, A., Uhlemann, C., Friedrich, O., et al. 2021, MNRAS, 505, 2886 [NASA ADS] [CrossRef] [Google Scholar]
  11. Bridle, S., & King, L. 2007, New J. Phys., 9, 444 [Google Scholar]
  12. Brown, M., Taylor, A., Hambly, N., & Dye, S. 2002, MNRAS, 333, 501 [NASA ADS] [CrossRef] [Google Scholar]
  13. Campagne, J. E., Lanusse, F., Zuntz, J., et al. 2023, Open J. Astrophys., 6 [Google Scholar]
  14. Catelan, P., Kamionkowski, M., & Blandford, R. D. 2001, MNRAS, 320, L7 [NASA ADS] [CrossRef] [Google Scholar]
  15. Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. K. 2018, Adv. Neural Inf. Process. Syst., 31 [Google Scholar]
  16. Cheng, S., & Ménard, B. 2021, MNRAS, 507, 1012 [NASA ADS] [CrossRef] [Google Scholar]
  17. Chisari, N. E., Alonso, D., Krause, E., et al. 2019, ApJS, 242, 2 [Google Scholar]
  18. Eisenstein, D. J., & Hu, W. 1998, ApJ, 496, 605 [Google Scholar]
  19. Feng, Y., Chu, M. Y., Seljak, U., & McDonald, P. 2019, Astrophysics Source Code Library [record ascl:1905.010] [Google Scholar]
  20. Fluri, J., Kacprzak, T., Lucchi, A., et al. 2019, Phys. Rev. D, 100, 063514 [Google Scholar]
  21. Fluri, J., Kacprzak, T., Refregier, A., Lucchi, A., & Hofmann, T. 2021, Phys. Rev. D, 104, 123526 [NASA ADS] [CrossRef] [Google Scholar]
  22. Fluri, J., Kacprzak, T., Lucchi, A., et al. 2022, Phys. Rev. D, 105, 083518 [NASA ADS] [CrossRef] [Google Scholar]
  23. Gatti, M., Jain, B., Chang, C., et al. 2022, Phys. Rev. D, 106, 083509 [NASA ADS] [CrossRef] [Google Scholar]
  24. Halder, A., Friedrich, O., Seitz, S., & Varga, T. N. 2021, MNRAS, 506, 2780 [CrossRef] [Google Scholar]
  25. Harnois-Déraps, J., Martinet, N., & Reischke, R. 2022, MNRAS, 509, 3868 [Google Scholar]
  26. Hartlap, J., Simon, P., & Schneider, P. 2007, A&A, 464, 399 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Heymans, C., Brown, M., Heavens, A., et al. 2004, MNRAS, 347, 895 [Google Scholar]
  28. Hilbert, S., Hartlap, J., White, S. D. M., & Schneider, P. 2009, A&A, 499, 31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Hirata, C. M., & Seljak, U. 2004, Phys. Rev. D, 70, 063526 [Google Scholar]
  30. Hockney, R., & Eastwood, J. 1988, Computer Simulation Using Particles (USA: Taylor& Francis, Inc.) [CrossRef] [Google Scholar]
  31. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  32. Jain, B., & Seljak, U. 1997, ApJ, 484, 560 [Google Scholar]
  33. Jain, B., Seljak, U., & White, S. 2000, ApJ, 530, 547 [NASA ADS] [CrossRef] [Google Scholar]
  34. Jeffrey, N., Alsing, J., & Lanusse, F. 2021, MNRAS, 501, 954 [Google Scholar]
  35. Kacprzak, T., & Fluri, J. 2022, Phys. Rev. X, 12, 031029 [NASA ADS] [Google Scholar]
  36. Kacprzak, T., Kirk, D., Friedrich, O., et al. 2016, MNRAS, 463, 3653 [Google Scholar]
  37. Kilbinger, M. 2015, Rep. Prog. Phys., 78, 086901 [Google Scholar]
  38. King, L. J., & Schneider, P. 2003, A&A, 398, 23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Kratochvil, J. M., Lim, E. A., Wang, S., et al. 2012, Phys. Rev. D, 85, 103513 [Google Scholar]
  40. Lanzieri, D., Lanusse, F., & Starck, J. L. 2022, Machine Learning for Astrophysics, proceedings of the Thirty-ninth International Conference on Machine Learning (ICML 2022), July 22nd, Baltimore, MD, online at https://ml4astro.github.io/icml2022, 60 [Google Scholar]
  41. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
  42. Li, Y., Modi, C., Jamieson, D., et al. 2022, ArXiv e-prints [arXiv:2211.09815] [Google Scholar]
  43. Lin, C.-A., & Kilbinger, M. 2015, A&A, 583, A70 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Lin, C.-A., Kilbinger, M., & Pires, S. 2016, A&A, 593, A88 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Liu, J., & Madhavacheril, M. S. 2019, Phys. Rev. D, 99, 083508 [Google Scholar]
  46. Liu, J., Petri, A., Haiman, Z., et al. 2015a, Phys. Rev. D, 91, 063507 [Google Scholar]
  47. Liu, X., Pan, C., Li, R., et al. 2015b, MNRAS, 450, 2888 [Google Scholar]
  48. Liu, J., Bird, S., Matilla, J. M. Z., et al. 2018, JCAP, 2018, 049 [CrossRef] [Google Scholar]
  49. Makinen, T. L., Charnock, T., Alsing, J., & Wandelt, B. D. 2021, JCAP, 2021, 049 [CrossRef] [Google Scholar]
  50. Mandelbaum, R., Eifler, T., Hložek, R., et al. 2018, ArXiv e-prints [arXiv:1809.01669] [Google Scholar]
  51. Martinet, N., Schneider, P., Hildebrandt, H., et al. 2018, MNRAS, 474, 712 [Google Scholar]
  52. Modi, C., Lanusse, F., & Seljak, U. 2021, Astron. Comput., 37, 100505 [NASA ADS] [CrossRef] [Google Scholar]
  53. Osato, K., Liu, J., & Haiman, Z. 2021, MNRAS, 502, 5593 [Google Scholar]
  54. Peel, A., Lin, C.-A., Lanusse, F., et al. 2017, A&A, 599, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Petri, A., Haiman, Z., Hui, L., May, M., & Kratochvil, J. M. 2013, Phys. Rev. D, 88, 123002 [Google Scholar]
  56. Petri, A., Haiman, Z., & May, M. 2017, Phys. Rev. D, 95, 123503 [NASA ADS] [CrossRef] [Google Scholar]
  57. Pontryagin, L., Boltyanski, V., Gamkrelidze, R., & Mishchenko, E. 1962, The Mathematical Theory of Optimal Processes (New York: Interscience) [Google Scholar]
  58. Porqueres, N., Heavens, A., Mortlock, D., & Lavaux, G. 2021, MNRAS, 502, 3035 [NASA ADS] [CrossRef] [Google Scholar]
  59. Rizzato, M., Benabed, K., Bernardeau, F., & Lacasa, F. 2019, MNRAS, 490, 4688 [NASA ADS] [CrossRef] [Google Scholar]
  60. Schneider, P. 2006, Gravitational Lensing: Strong, Weak and Micro (Springer), 269 [NASA ADS] [CrossRef] [Google Scholar]
  61. Schneider, P., & Lombardi, M. 2003, A&A, 397, 809 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Seitz, S., Schneider, P., & Ehlers, J. 1994, CQG, 11, 2345 [NASA ADS] [CrossRef] [Google Scholar]
  63. Semboloni, E., Schrabback, T., van Waerbeke, L., et al. 2011, MNRAS, 410, 143 [Google Scholar]
  64. Shan, H., Liu, X., Hildebrandt, H., et al. 2018, MNRAS, 474, 1116 [Google Scholar]
  65. Smith, R. E., Peacock, J. A., Jenkins, A., et al. 2003, MNRAS, 341, 1311 [Google Scholar]
  66. Spergel, D., Gehrels, N., Baltay, C., et al. 2015, ArXiv e-prints [arXiv:1503.03757] [Google Scholar]
  67. Springel, V. 2010, MNRAS, 401, 791 [Google Scholar]
  68. Starck, J.-L., Fadili, J., & Murtagh, F. 2007, IEEE Trans. Image Process., 16, 297 [Google Scholar]
  69. Starck, J.-L., Murtagh, F., & Fadili, J. 2010, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity (USA: Cambridge University Press) [CrossRef] [Google Scholar]
  70. Takada, M., & Jain, B. 2004, MNRAS, 348, 897 [Google Scholar]
  71. Takahashi, R., Sato, M., Nishimichi, T., Taruya, A., & Oguri, M. 2012, ApJ, 761, 152 [Google Scholar]
  72. Tegmark, M., Taylor, A. N., & Heavens, A. F. 1997, ApJ, 480, 22 [NASA ADS] [CrossRef] [Google Scholar]
  73. Troxel, M., & Ishak, M. 2015, Phys. Rep., 558, 1 [NASA ADS] [CrossRef] [Google Scholar]
  74. Uhlemann, C., Friedrich, O., Villaescusa-Navarro, F., Banerjee, A., & Codis, S. 2020, MNRAS, 495, 4006 [NASA ADS] [CrossRef] [Google Scholar]
  75. Vale, C., & White, M. 2003, ApJ, 592, 699 [CrossRef] [Google Scholar]
  76. van Waerbeke, L., Bernardeau, F., & Mellier, Y. 1999, A&A, 342, 15 [NASA ADS] [Google Scholar]
  77. Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., et al. 2021, ApJ, 915, 71 [NASA ADS] [CrossRef] [Google Scholar]
  78. Zeghal, J., Lanusse, F., Boucaud, A., Remy, B., & Aubourg, E. 2022, Machine Learning for Astrophysics, proceedings of the Thirty-ninth International Conference on Machine Learning (ICML 2022), July 22nd, Baltimore, MD, online at https://ml4astro.github.io/icml2022, 52 [Google Scholar]
  79. Zhang, Z., Chang, C., Larsen, P., et al. 2022, MNRAS, 514, 2181 [NASA ADS] [CrossRef] [Google Scholar]
  80. Zürcher, D., Fluri, J., Sgier, R., et al. 2022, MNRAS, 511, 2075 [CrossRef] [Google Scholar]

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