Open Access
Issue
A&A
Volume 701, September 2025
Article Number A284
Number of page(s) 13
Section Cosmology (including clusters of galaxies)
DOI https://doi.org/10.1051/0004-6361/202555887
Published online 26 September 2025
  1. AbdusSalam, S., Abel, S., & Romão, M. C. 2025, Phys. Rev. D, 111, 015022 [Google Scholar]
  2. Amon, A., & Efstathiou, G. 2022, MNRAS, 516, 5355 [CrossRef] [Google Scholar]
  3. Aricò, G., Angulo, R. E., Hernández-Monteagudo, C., et al. 2020, MNRAS, 495, 4800 [Google Scholar]
  4. Aricò, G., Angulo, R. E., Contreras, S., et al. 2021a, MNRAS, 506, 4070 [CrossRef] [Google Scholar]
  5. Aricò, G., Angulo, R. E., Hernández-Monteagudo, C., Contreras, S., & Zennaro, M. 2021b, MNRAS, 503, 3596 [Google Scholar]
  6. Aricò, G., Angulo, R. E., Zennaro, M., et al. 2023, A&A, 678, A109 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Bartlett, D. J., Desmond, H., & Ferreira, P. G. 2023, in The Genetic and Evolutionary Computation Conference 2023 [Google Scholar]
  8. Bartlett, D. J., Kammerer, L., Kronberger, G., et al. 2024a, A&A, 686, A209 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Bartlett, D. J., Wandelt, B. D., Zennaro, M., Ferreira, P. G., & Desmond, H. 2024b, A&A, 686, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Bassini, L., Rasia, E., Borgani, S., et al. 2020, A&A, 642, A37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Bielefeld, J., Huterer, D., & Linder, E. V. 2015, JCAP, 2015, 023 [Google Scholar]
  12. Bigwood, L., Amon, A., Schneider, A., et al. 2024, MNRAS, 534, 655 [NASA ADS] [CrossRef] [Google Scholar]
  13. Bird, S., Viel, M., & Haehnelt, M. G. 2012, MNRAS, 420, 2551 [Google Scholar]
  14. Bragança, D. P. L., Lewandowski, M., Sekera, D., Senatore, L., & Sgier, R. 2021, JCAP, 2021, 074 [Google Scholar]
  15. Burlacu, B. 2023, Proceedings of the Companion Conference on Genetic and Evolutionary Computation, GECCO ’23 Companion (New York, NY, USA: Association for Computing Machinery), 2412 [Google Scholar]
  16. Burlacu, B., Kronberger, G., & Kommenda, M. 2020, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, GECCO ’20 (New York, NY, USA: Association for Computing Machinery), 1562 [Google Scholar]
  17. Cava, W. G. L., Orzechowski, P., Burlacu, B., et al. 2021, arXiv e-prints [arXiv:2107.14351] [Google Scholar]
  18. Chen, A., Aricò, G., Huterer, D., et al. 2023, MNRAS, 518, 5340 [Google Scholar]
  19. Chisari, N. E., Richardson, M. L. A., Devriendt, J., et al. 2018, MNRAS, 480, 3962 [Google Scholar]
  20. Crain, R. A., Schaye, J., Bower, R. G., et al. 2015, MNRAS, 450, 1937 [NASA ADS] [CrossRef] [Google Scholar]
  21. Dai, B., Feng, Y., & Seljak, U. 2018, JCAP, 2018, 009 [Google Scholar]
  22. Davé, R., Anglés-Alcázar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827 [Google Scholar]
  23. Debackere, S. N. B., Schaye, J., & Hoekstra, H. 2020, MNRAS, 492, 2285 [Google Scholar]
  24. Delgado, A. M., Anglés-Alcázar, D., Thiele, L., et al. 2023, MNRAS, 526, 5306 [Google Scholar]
  25. Dolag, K., Mevius, E., & Remus, R.-S. 2017, Galaxies, 5, 35 [NASA ADS] [CrossRef] [Google Scholar]
  26. Dubois, Y., Pichon, C., Welker, C., et al. 2014, MNRAS, 444, 1453 [Google Scholar]
  27. Eifler, T., Krause, E., Dodelson, S., et al. 2015, MNRAS, 454, 2451 [Google Scholar]
  28. Fedeli, C. 2014, JCAP, 2014, 028 [CrossRef] [Google Scholar]
  29. Fedeli, C., Semboloni, E., Velliscig, M., et al. 2014, JCAP, 2014, 028 [Google Scholar]
  30. Feng, Y., Di-Matteo, T., Croft, R. A., et al. 2016, MNRAS, 455, 2778 [NASA ADS] [CrossRef] [Google Scholar]
  31. Foreman, S., Becker, M. R., & Wechsler, R. H. 2016, MNRAS, 463, 3326 [NASA ADS] [CrossRef] [Google Scholar]
  32. García-García, C., Zennaro, M., Aricò, G., Alonso, D., & Angulo, R. E. 2024, JCAP, 08, 024 [Google Scholar]
  33. Gebhardt, M., Anglés-Alcázar, D., Borrow, J., et al. 2024, MNRAS, 529, 4896 [NASA ADS] [CrossRef] [Google Scholar]
  34. Giri, S. K., & Schneider, A. 2021, JCAP, 2021, 046 [Google Scholar]
  35. Hadzhiyska, B., Ferraro, S., Ried Guachalla, B., et al. 2024, arXiv e-prints [arXiv:2407.07152] [Google Scholar]
  36. Hikage, C., Oguri, M., Hamana, T., et al. 2019, PASJ, 71, 43 [Google Scholar]
  37. Hopkins, P. F. 2015, MNRAS, 450, 53 [Google Scholar]
  38. Huang, H.-J., Eifler, T., Mandelbaum, R., & Dodelson, S. 2019, MNRAS, 488, 1652 [NASA ADS] [CrossRef] [Google Scholar]
  39. Jo, Y., Genel, S., Sengupta, A., et al. 2025, arXiv e-prints [arXiv:2502.13239] [Google Scholar]
  40. Kodwani, D., Alonso, D., & Ferreira, P. 2019, Open J. Astrophys., 2, 3 [NASA ADS] [CrossRef] [Google Scholar]
  41. Köhlinger, F., Viola, M., Valkenburg, W., et al. 2016, MNRAS, 456, 1508 [Google Scholar]
  42. Köhlinger, F., Viola, M., Joachimi, B., et al. 2017, MNRAS, 471, 4412 [Google Scholar]
  43. Kommenda, M., Burlacu, B., Kronberger, G., & Affenzeller, M. 2020, Genet. Program. Evolvable Mach., 21, 471 [Google Scholar]
  44. Kronberger, G., Burlacu, B., Kommenda, M., Winkler, S. M., & Affenzeller, M. 2024, Symbolic Regression (Chapman& Hall/CRC Press) [Google Scholar]
  45. Kugel, R., Schaye, J., Schaller, M., et al. 2023, MNRAS, 526, 6103 [NASA ADS] [CrossRef] [Google Scholar]
  46. Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. 2002, Evol. Comput., 10, 263 [CrossRef] [Google Scholar]
  47. Lee, J., Shin, J., Snaith, O. N., et al. 2021, ApJ, 908, 11 [CrossRef] [Google Scholar]
  48. Levenberg, K. 1944, Q. Appl. Math., 2, 164 [Google Scholar]
  49. Lewandowski, M., Perko, A., & Senatore, L. 2015, JCAP, 2015, 019 [Google Scholar]
  50. Lovell, C. C., Starkenburg, T., Ho, M., et al. 2024, arXiv e-prints [arXiv:2411.13960] [Google Scholar]
  51. Lu, T., & Haiman, Z. 2021, MNRAS, 506, 3406 [NASA ADS] [CrossRef] [Google Scholar]
  52. Maraio, A., Hall, A., & Taylor, A. 2025, MNRAS, 537, 1749 [Google Scholar]
  53. Marquardt, D. W. 1963, J. Soc. Indust. Appl. Math., 11, 431 [CrossRef] [Google Scholar]
  54. Martin-Alvarez, S., Iršic, V., Koudmani, S., et al. 2024, Stirring the cosmic pot: how black hole feedback shapes the matter power spectrum in the Fable simulations (Oxford University Press) [Google Scholar]
  55. McCarthy, I. G., Schaye, J., Bird, S., & Le Brun, A. M. C. 2017, MNRAS, 465, 2936 [Google Scholar]
  56. Mead, A. J., Peacock, J. A., Heymans, C., Joudaki, S., & Heavens, A. F. 2015, MNRAS, 454, 1958 [NASA ADS] [CrossRef] [Google Scholar]
  57. Mead, A. J., Brieden, S., Tröster, T., & Heymans, C. 2021, MNRAS, 502, 1401 [Google Scholar]
  58. Mohammed, I., Martizzi, D., Teyssier, R., & Amara, A. 2014, arXiv e-prints [arXiv:1410.6826] [Google Scholar]
  59. Nelson, D., Springel, V., Pillepich, A., et al. 2019, Computat. Astrophys. Cosmol., 6, 2 [NASA ADS] [CrossRef] [Google Scholar]
  60. Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670 [NASA ADS] [CrossRef] [Google Scholar]
  61. Ni, Y., Genel, S., Anglés-Alcázar, D., et al. 2023, ApJ, 959, 136 [NASA ADS] [CrossRef] [Google Scholar]
  62. Pandey, S., Lehman, K., Baxter, E. J., et al. 2023, MNRAS, 525, 1779 [CrossRef] [Google Scholar]
  63. Pillepich, A., Springel, V., Nelson, D., et al. 2018, MNRAS, 473, 4077 [Google Scholar]
  64. Preston, C., Amon, A., & Efstathiou, G. 2023, MNRAS, 525, 5554 [NASA ADS] [CrossRef] [Google Scholar]
  65. Salcido, J., McCarthy, I. G., Kwan, J., Upadhye, A., & Font, A. S. 2023, MNRAS, 523, 2247 [NASA ADS] [CrossRef] [Google Scholar]
  66. Schaller, M., & Schaye, J. 2025, MNRAS, 540, 2322 [Google Scholar]
  67. Schaller, M., Borrow, J., Draper, P. W., et al. 2024, MNRAS, 530, 2378 [NASA ADS] [CrossRef] [Google Scholar]
  68. Schaller, M., Schaye, J., Kugel, R., Broxterman, J. C., & van Daalen, M. P. 2025, MNRAS, 539, 1337 [Google Scholar]
  69. Schaye, J., Dalla Vecchia, C., Booth, C. M., et al. 2010, MNRAS, 402, 1536 [Google Scholar]
  70. Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521 [Google Scholar]
  71. Schaye, J., Kugel, R., Schaller, M., et al. 2023, MNRAS, 526, 4978 [NASA ADS] [CrossRef] [Google Scholar]
  72. Schneider, A., & Teyssier, R. 2015, JCAP, 2015, 049 [Google Scholar]
  73. Schneider, A., Teyssier, R., Stadel, J., et al. 2019, JCAP, 2019, 020 [Google Scholar]
  74. Schneider, A., Refregier, A., Grandis, S., et al. 2020, JCAP, 2020, 020 [CrossRef] [Google Scholar]
  75. Semboloni, E., Hoekstra, H., Schaye, J., van Daalen, M. P., & McCarthy, I. G. 2011, MNRAS, 417, 2020 [Google Scholar]
  76. Semboloni, E., Hoekstra, H., & Schaye, J. 2013, MNRAS, 434, 148 [CrossRef] [Google Scholar]
  77. Shao, H., Villaescusa-Navarro, F., Villanueva-Domingo, P., et al. 2023, ApJ, 944, 27 [Google Scholar]
  78. Sharma, D., Dai, B., Villaescusa-Navarro, F., & Seljak, U. 2025, MNRAS, 538, 1415 [Google Scholar]
  79. Somerville, R. S., & Davé, R. 2015, ARA&A, 53, 51 [Google Scholar]
  80. Springel, V. 2010, MNRAS, 401, 791 [Google Scholar]
  81. Springel, V., Pakmor, R., Zier, O., & Reinecke, M. 2021, MNRAS, 506, 2871 [NASA ADS] [CrossRef] [Google Scholar]
  82. Sui, C., Bartlett, D. J., Pandey, S., et al. 2025, A&A, 698, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Sullivan, J. M., Seljak, U., & Singh, S. 2021, JCAP, 2021, 026 [CrossRef] [Google Scholar]
  84. Terasawa, R., Li, X., Takada, M., et al. 2025, Phys. Rev. D, 111, 063509 [Google Scholar]
  85. Tröster, T., Ferguson, C., Harnois-Déraps, J., & McCarthy, I. G. 2019, MNRAS, 487, L24 [Google Scholar]
  86. van Daalen, M. P., Schaye, J., Booth, C. M., & Dalla Vecchia, C. 2011, MNRAS, 415, 3649 [Google Scholar]
  87. van Daalen, M. P., McCarthy, I. G., & Schaye, J. 2020, MNRAS, 491, 2424 [Google Scholar]
  88. Villaescusa-Navarro, F. 2018, Pylians: Python libraries for the analysis of numerical simulations, Astrophysics Source Code Library [record ascl:1811.008] [Google Scholar]
  89. Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., et al. 2021a, ApJ, 915, 71 [NASA ADS] [CrossRef] [Google Scholar]
  90. Villaescusa-Navarro, F., Genel, S., Angles-Alcazar, D., et al. 2021b, arXiv e-prints [arXiv:2109.10360] [Google Scholar]
  91. Villaescusa-Navarro, F., Wandelt, B. D., Anglés-Alcázar, D., et al. 2022, ApJ, 928, 44 [NASA ADS] [CrossRef] [Google Scholar]
  92. Villaescusa-Navarro, F., Genel, S., Anglés-Alcázar, D., et al. 2023, ApJS, 265, 54 [NASA ADS] [CrossRef] [Google Scholar]
  93. Villanueva-Domingo, P., & Villaescusa-Navarro, F. 2022, ApJ, 937, 115 [NASA ADS] [CrossRef] [Google Scholar]
  94. Villanueva-Domingo, P., Villaescusa-Navarro, F., Anglés-Alcázar, D., et al. 2022, ApJ, 935, 30 [NASA ADS] [CrossRef] [Google Scholar]
  95. Vogelsberger, M., Genel, S., Springel, V., et al. 2014, Nature, 509, 177 [Google Scholar]
  96. Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, Nat. Rev. Phys., 2, 42 [Google Scholar]
  97. Wadekar, D., Thiele, L., Hill, J. C., et al. 2023a, MNRAS, 522, 2628 [NASA ADS] [CrossRef] [Google Scholar]
  98. Wadekar, D., Thiele, L., Villaescusa-Navarro, F., et al. 2023b, Proc. Nat. Acad. Sci., 120, e2202074120 [NASA ADS] [CrossRef] [Google Scholar]
  99. Weinberger, R., Springel, V., Hernquist, L., et al. 2017, MNRAS, 465, 3291 [Google Scholar]
  100. Weinberger, R., Springel, V., & Pakmor, R. 2020, ApJS, 248, 32 [Google Scholar]
  101. White, M. 2004, Astropart. Phys., 22, 211 [Google Scholar]
  102. Zhan, H., & Knox, L. 2004, ApJ, 616, L75 [Google Scholar]

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