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Home All issues Volume 684 (April 2024) A&A, 684 (2024) A100 References
Open Access
Issue
A&A
Volume 684, April 2024
Article Number A100
Number of page(s) 13
Section Cosmology (including clusters of galaxies)
DOI https://doi.org/10.1051/0004-6361/202347929
Published online 11 April 2024
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2016, arXiv e-prints [arXiv:1603.04467] [Google Scholar]
  2. Abdar, M., Pourpanah, F., Hussain, S., et al. 2021, Inf. Fusion, 76, 243 [CrossRef] [Google Scholar]
  3. Alves de Oliveira, R., Li, Y., Villaescusa-Navarro, F., Ho, S., & Spergel, D. N. 2020, ArXiv e-prints [arXiv:2012.00240] [Google Scholar]
  4. Beaumont, M. A. 2010, Annu. Rev. Ecol. Evol. Syst., 41, 379 [Google Scholar]
  5. Berti, E., Barausse, E., Cardoso, V., et al. 2015, Class. Quant. Grav., 32, 243001 [NASA ADS] [CrossRef] [Google Scholar]
  6. Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. 2015, Proceedings of the 32nd International Conference on International Conference on Machine Learning – Volume 37, ICML’15 (JMLR), 1613 [Google Scholar]
  7. Bos, E. G. P., van de Weygaert, R., Dolag, K., & Pettorino, V. 2012, MNRAS, 426, 440 [NASA ADS] [CrossRef] [Google Scholar]
  8. Brown, Z., Mishtaku, G., & Demina, R. 2022, A&A, 667, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Cai, Y.-C., Padilla, N., & Li, B. 2015, MNRAS, 451, 1036 [NASA ADS] [CrossRef] [Google Scholar]
  10. Charnock, T., Perreault-Levasseur, L., & Lanusse, F. 2022, Bayesian Neural Networks (World Scientific), 663 [Google Scholar]
  11. Contarini, S., Marulli, F., Moscardini, L., et al. 2021, MNRAS, 504, 5021 [NASA ADS] [CrossRef] [Google Scholar]
  12. Crocce, M., Pueblas, S., & Scoccimarro, R. 2006, MNRAS, 373, 369 [NASA ADS] [CrossRef] [Google Scholar]
  13. Crocce, M., Pueblas, S., & Scoccimarro, R. 2012, Astrophysics Source Code Library [record ascl:1201.005] [Google Scholar]
  14. Csilléry, K., Blum, M. G., Gaggiotti, O. E., & François, O. 2010, Trends Ecol. Evol., 25, 410 [CrossRef] [Google Scholar]
  15. De Felice, A., & Tsujikawa, S. 2010, Liv. Rev. Relat., 13, 3 [NASA ADS] [CrossRef] [Google Scholar]
  16. Deng, W., Zhang, X., Liang, F., & Lin, G. 2019, Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation, https://openreview.net/forum?id=S1grRoR9tQ [Google Scholar]
  17. Denker, J. S., & LeCun, Y. 1990, Proceedings of the 3rd International Conference on Neural Information Processing Systems, NIPS’90 (San Francisco: Morgan Kaufmann Publishers Inc.), 853 [Google Scholar]
  18. Dinh, L., Sohl-Dickstein, J., & Bengio, S. 2017, International Conference on Learning Representations, https://openreview.net/forum?id=HkpbnH9lx [Google Scholar]
  19. Dong, F., Park, C., Hong, S. E., et al. 2023, ApJ, 953, 98 [NASA ADS] [CrossRef] [Google Scholar]
  20. Dvorkin, C., Mishra-Sharma, S., Nord, B., et al. 2022, ArXiv e-prints [arXiv:2203.08056] [Google Scholar]
  21. Fang, W., Li, B., & Zhao, G.-B. 2017, Phys. Rev. Lett., 118, 181301 [NASA ADS] [CrossRef] [Google Scholar]
  22. Fiedler, F., & Lucia, S. 2023, IEEE Access, 11, 123149 [NASA ADS] [CrossRef] [Google Scholar]
  23. Fluri, J., Kacprzak, T., Sgier, R., Refregier, A., & Amara, A. 2018, JCAP, 2018, 051 [CrossRef] [Google Scholar]
  24. Fortuin, V., Garriga-Alonso, A., Ober, S. W., et al. 2021, ArXiv e-prints [arXiv:2102.06571] [Google Scholar]
  25. Gal, Y. 2016, PhD Thesis, University of Cambridge, UK [Google Scholar]
  26. Gal, Y., & Ghahramani, Z. 2016, in Proceedings of the 33rd International Conference on Machine Learning, eds. M. F. Balcan, & K. Q. Weinberger (New York: PMLR), Proc. Mach. Learn. Res., 48, 1050 [Google Scholar]
  27. García-Farieta, J. E., Marulli, F., Veropalumbo, A., et al. 2019, MNRAS, 488, 1987 [Google Scholar]
  28. García-Farieta, J. E., Marulli, F., Moscardini, L., Veropalumbo, A., & Casas-Miranda, R. A. 2020, MNRAS, 494, 1658 [CrossRef] [Google Scholar]
  29. García-Farieta, J. E., Hellwing, W. A., Gupta, S., & Bilicki, M. 2021, Phys. Rev. D, 103, 103524 [CrossRef] [Google Scholar]
  30. Graves, A. 2011, Practical Variational Inference for Neural Networks (Curran Associates, Inc.), 24 [Google Scholar]
  31. Gunapati, G., Jain, A., Srijith, P. K., & Desai, S. 2022, PASA, 39, e001 [NASA ADS] [CrossRef] [Google Scholar]
  32. Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. 2017, Proceedings of the 34th International Conference on Machine Learning – Volume 70, ICML’17 (JMLR), 1321 [Google Scholar]
  33. Gupta, S., Hellwing, W. A., Bilicki, M., & García-Farieta, J. E. 2022, Phys. Rev. D, 105, 043538 [NASA ADS] [CrossRef] [Google Scholar]
  34. Hagstotz, S., Costanzi, M., Baldi, M., & Weller, J. 2019, MNRAS, 486, 3927 [Google Scholar]
  35. Hahn, C., Eickenberg, M., Ho, S., et al. 2022, ArXiv e-prints [arXiv:2211.00723] [Google Scholar]
  36. Hamaus, N., Pisani, A., Sutter, P. M., et al. 2016, Phys. Rev. Lett., 117, 091302 [NASA ADS] [CrossRef] [Google Scholar]
  37. Harnois-Déraps, J., Martinet, N., Castro, T., et al. 2021, MNRAS, 506, 1623 [CrossRef] [Google Scholar]
  38. Henning, C., D’Angelo, F., & Grewe, B. F. 2021, ArXiv e-prints [arXiv:2107.12248] [Google Scholar]
  39. Hernández-Aguayo, C., Hou, J., Li, B., Baugh, C. M., & Sánchez, A. G. 2019, MNRAS, 485, 2194 [CrossRef] [Google Scholar]
  40. Hernández-Aguayo, C., Ruan, C.-Z., Li, B., et al. 2022, JCAP, 2022, 048 [CrossRef] [Google Scholar]
  41. Hikage, C., Schmalzing, J., Buchert, T., et al. 2003, PASJ, 55, 911 [NASA ADS] [Google Scholar]
  42. Hockney, R. W., & Eastwood, J. W. 1981, Computer Simulation Using Particles (New York: McGraw-Hill) [Google Scholar]
  43. Hortua, H. J. 2021, ArXiv e-prints [arXiv:2112.11865] [Google Scholar]
  44. Hortúa, H. J., Volpi, R., Marinelli, D., & Malagò, L. 2020a, Phys. Rev. D, 102, 103509 [CrossRef] [Google Scholar]
  45. Hortúa, H. J., Malagò, L., & Volpi, R. 2020b, Mach. Learn.: Sci. Technol., 1, 035014 [CrossRef] [Google Scholar]
  46. Hortúa, H. J., García, L., & Castaneda, L. 2023, Front. Astron. Space Sci., 10, 1139120 [CrossRef] [Google Scholar]
  47. Howlett, C., Manera, M., & Percival, W. J. 2015, Astron. Comput., 12, 109 [NASA ADS] [CrossRef] [Google Scholar]
  48. Hu, W., & Sawicki, I. 2007, Phys. Rev. D, 76, 064004 [CrossRef] [Google Scholar]
  49. Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. 2017, ArXiv e-prints [arXiv:1709.01507] [Google Scholar]
  50. Ivarsen, M. F., Bull, P., Llinares, C., & Mota, D. 2016, A&A, 595, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Jennings, E., Baugh, C. M., Li, B., Zhao, G.-B., & Koyama, K. 2012, MNRAS, 425, 2128 [NASA ADS] [CrossRef] [Google Scholar]
  52. Johnson, A., Blake, C., Dossett, J., et al. 2016, MNRAS, 458, 2725 [NASA ADS] [CrossRef] [Google Scholar]
  53. Kacprzak, T., Kirk, D., Friedrich, O., et al. 2016, MNRAS, 463, 3653 [Google Scholar]
  54. Kilbinger, M. 2015, Rep. Progr. Phys., 78, 086901 [Google Scholar]
  55. Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
  56. Kitaura, F.-S., Ata, M., Rodríguez-Torres, S. A., et al. 2021, MNRAS, 502, 3456 [NASA ADS] [CrossRef] [Google Scholar]
  57. Koda, J., Blake, C., Beutler, F., Kazin, E., & Marin, F. 2016, MNRAS, 459, 2118 [NASA ADS] [CrossRef] [Google Scholar]
  58. Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. 2020, MNRAS, 495, 4227 [NASA ADS] [CrossRef] [Google Scholar]
  59. Kostić, A., Nguyen, N.-M., Schmidt, F., & Reinecke, M. 2023, JCAP, 2023, 063 [Google Scholar]
  60. Kratochvil, J. M., Lim, E. A., Wang, S., et al. 2012, Phys. Rev. D, 85, 103513 [Google Scholar]
  61. Lakshminarayanan, B., Pritzel, A., & Blundell, C. 2017, Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook: Curran Associates Inc.), 6405 [Google Scholar]
  62. Laszlo, I., & Bean, R. 2008, Phys. Rev. D, 77, 024048 [CrossRef] [Google Scholar]
  63. Lavaux, G., & Wandelt, B. D. 2010, MNRAS, 403, 1392 [NASA ADS] [CrossRef] [Google Scholar]
  64. Laves, M. H., Ihler, S., Fast, J. F., Kahrs, L. A., & Ortmaier, T. 2020, Medical Imaging with Deep Learning, https://openreview.net/forum?id=CecZ_0t79q [Google Scholar]
  65. Lazanu, A. 2021, JCAP, 2021, 039 [CrossRef] [Google Scholar]
  66. Lemos, P., Cranmer, M., Abidi, M., et al. 2023a, Mach. Learn.: Sci. Technol., 4, 01LT01 [NASA ADS] [CrossRef] [Google Scholar]
  67. Lemos, P., Parker, L. H., Hahn, C., et al. 2023b, Machine Learning for Astrophysics, Workshop at the Fortieth International Conference on Machine Learning (ICML 2023), July 29th, Hawaii, USA, online at https://ml4astro.github.io/icml2023/, 18 [Google Scholar]
  68. Lewis, A. 2019, ArXiv e-prints [arXiv:1910.13970] [Google Scholar]
  69. Li, B., Zhao, G.-B., Teyssier, R., & Koyama, K. 2012, JCAP, 2012, 051 [CrossRef] [Google Scholar]
  70. Li, X.-D., Park, C., Sabiu, C. G., & Kim, J. 2015, MNRAS, 450, 807 [NASA ADS] [CrossRef] [Google Scholar]
  71. Louizos, C., & Welling, M. 2017, Proceedings of the 34th International Conference on Machine Learning – Volume 70, ICML’17 (JMLR), 2218 [Google Scholar]
  72. Luo, X., Wu, Z., Li, M., et al. 2019, ApJ, 887, 125 [NASA ADS] [CrossRef] [Google Scholar]
  73. Lyall, S., Blake, C., Turner, R., Ruggeri, R., & Winther, H. 2023, MNRAS, 518, 5929 [Google Scholar]
  74. Mancarella, M., Kennedy, J., Bose, B., & Lombriser, L. 2022, Phys. Rev. D, 105, 023531 [NASA ADS] [CrossRef] [Google Scholar]
  75. McCarthy, I. G., Salcido, J., Schaye, J., et al. 2023, MNRAS, 526, 5494 [NASA ADS] [CrossRef] [Google Scholar]
  76. Merten, J., Giocoli, C., Baldi, M., et al. 2019, MNRAS, 487, 104 [Google Scholar]
  77. Moresco, M., Amati, L., Amendola, L., et al. 2022, Liv. Rev. Relat., 25, 6 [NASA ADS] [Google Scholar]
  78. Nguyen, N.-M., Huterer, D., & Wen, Y. 2023, Phys. Rev. Lett., 131, 111001 [CrossRef] [Google Scholar]
  79. Nojiri, S., Odintsov, S. D., & Oikonomou, V. K. 2017, Phys. Rep., 692, 1 [NASA ADS] [CrossRef] [Google Scholar]
  80. Odintsov, S. D., Oikonomou, V. K., Giannakoudi, I., Fronimos, F. P., & Lymperiadou, E. C. 2023, Symmetry, 15, 1701 [NASA ADS] [CrossRef] [Google Scholar]
  81. Paillas, E., Cai, Y.-C., Padilla, N., & Sánchez, A. G. 2021, MNRAS, 505, 5731 [NASA ADS] [CrossRef] [Google Scholar]
  82. Pan, S., Liu, M., Forero-Romero, J., et al. 2020, Sci. China Phys. Mech. Astron., 63, 110412 [NASA ADS] [CrossRef] [Google Scholar]
  83. Park, C., & Kim, Y.-R. 2010, ApJ, 715, L185 [NASA ADS] [CrossRef] [Google Scholar]
  84. Peebles, P. J. E. 2001, in Historical Development of Modern Cosmology, eds. V. J. Martínez, V. Trimble, & M. J. Pons-Bordería, ASP Conf. Ser., 252, 201 [NASA ADS] [Google Scholar]
  85. Peel, A., Lin, C.-A., Lanusse, F., et al. 2017, A&A, 599, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Peel, A., Lalande, F., Starck, J.-L., et al. 2019, Phys. Rev. D, 100, 023508 [Google Scholar]
  87. Perico, E. L. D., Voivodic, R., Lima, M., & Mota, D. F. 2019, A&A, 632, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Philcox, O. H. E., Slepian, Z., Hou, J., et al. 2022, MNRAS, 509, 2457 [Google Scholar]
  89. Planck Collaboration VI. 2020, A&A, 641, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Puchwein, E., Baldi, M., & Springel, V. 2013, MNRAS, 436, 348 [NASA ADS] [CrossRef] [Google Scholar]
  91. Ravanbakhsh, S., Oliva, J., Fromenteau, S., et al. 2017, ArXiv e-prints [arXiv:1711.02033] [Google Scholar]
  92. Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., & Winther, O. 2016, Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS’16 (Red Hook: Curran Associates Inc.), 3745 [Google Scholar]
  93. Song, Y.-S., Hu, W., & Sawicki, I. 2007, Phys. Rev. D, 75, 044004 [CrossRef] [Google Scholar]
  94. Sunnåker, M., Busetto, A. G., Numminen, E., et al. 2013, PLOS Comput. Biol., 9, 1 [Google Scholar]
  95. Takada, M., & Jain, B. 2003, MNRAS, 340, 580 [NASA ADS] [CrossRef] [Google Scholar]
  96. Tamosiunas, A., Winther, H. A., Koyama, K., et al. 2021, MNRAS, 506, 3049 [NASA ADS] [CrossRef] [Google Scholar]
  97. Tassev, S., Zaldarriaga, M., & Eisenstein, D. J. 2013, JCAP, 2013, 036 [Google Scholar]
  98. Thomas, O., Dutta, R., Corander, J., Kaski, S., & Gutmann, M. U. 2016, ArXiv e-prints [arXiv:1611.10242] [Google Scholar]
  99. Touati, A., Satija, H., Romoff, J., Pineau, J., & Vincent, P. 2018, ArXiv e-prints [arXiv:1806.02315] [Google Scholar]
  100. Tsujikawa, S. 2008, Phys. Rev. D, 77, 023507 [NASA ADS] [CrossRef] [Google Scholar]
  101. Tsujikawa, S., Uddin, K., Mizuno, S., Tavakol, R., & Yokoyama, J. 2008, Phys. Rev. D, 77, 103009 [NASA ADS] [CrossRef] [Google Scholar]
  102. Van Waerbeke, L., Mellier, Y., Radovich, M., et al. 2001, A&A, 374, 757 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  103. Veropalumbo, A., Binetti, A., Branchini, E., et al. 2022, JCAP, 2022, 033 [CrossRef] [Google Scholar]
  104. Villaescusa-Navarro, F., Hahn, C., Massara, E., et al. 2020, ApJS, 250, 2 [CrossRef] [Google Scholar]
  105. Voivodic, R., Lima, M., Llinares, C., & Mota, D. F. 2017, Phys. Rev. D, 95, 024018 [CrossRef] [Google Scholar]
  106. Watson, J., Andreas Lin, J., Klink, P., Pajarinen, J., & Peters, J. 2021, in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, eds. A. Banerjee, & K. Fukumizu (PMLR), Proc. Mach. Learn. Res., 130, 1198 [Google Scholar]
  107. Weinberg, D. H., Mortonson, M. J., Eisenstein, D. J., et al. 2013, Phys. Rep., 530, 87 [Google Scholar]
  108. Wen, Y., Vicol, P., Ba, J., Tran, D., & Grosse, R. 2018, International Conference on Learning Representations, https://openreview.net/forum?id=rJNpifWAb [Google Scholar]
  109. Winther, H. A., Koyama, K., Manera, M., Wright, B. S., & Zhao, G.-B. 2017, JCAP, 2017, 006 [CrossRef] [Google Scholar]
  110. Zhang, Z., Gu, G., Wang, X., et al. 2019, ApJ, 878, 137 [NASA ADS] [CrossRef] [Google Scholar]
  111. Zhang, H., Samushia, L., Brooks, D., et al. 2022, MNRAS, 515, 6133 [NASA ADS] [CrossRef] [Google Scholar]

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