Open Access
Issue
A&A
Volume 687, July 2024
Article Number A238
Number of page(s) 28
Section Cosmology (including clusters of galaxies)
DOI https://doi.org/10.1051/0004-6361/202449447
Published online 16 July 2024
  1. Agresti, A., & Coull, B. A. 1998, TAS, 52, 119 [Google Scholar]
  2. Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892 [Google Scholar]
  3. Aird, J., Coil, A. L., & Georgakakis, A. 2018, MNRAS, 474, 1225 [NASA ADS] [CrossRef] [Google Scholar]
  4. Anderson, M. E., Gaspari, M., White, S. D. M., Wang, W., & Dai, X. 2015, MNRAS, 449, 3806 [NASA ADS] [CrossRef] [Google Scholar]
  5. Artis, E., Ghirardini, V., Bulbul, E., et al. 2024, A&A, submitted [arXiv:2402.08459] [Google Scholar]
  6. Astropy Collaboration (Price-Whelan, A. M., et al.) 2022, ApJ, 935, 167 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bahar, Y. E., Bulbul, E., Clerc, N., et al. 2022, A&A, 661, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  8. Bahar, Y. E., Ghirardini, V., Sanders, J. S., et al. 2024, A&A, submitted [arXiv:2401.17276] [Google Scholar]
  9. Bayer, J., Osendorfer, C., Diot-Girard, S., Rueckstiess, T., & Urban, S. 2015, TUM, Tech. Rep., https://github.com/BRML/climin [Google Scholar]
  10. Bleem, L. E., Bocquet, S., Stalder, B., et al. 2020, ApJS, 247, 25 [Google Scholar]
  11. Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. 2017, J. Am. Stat. Assoc., 112, 859 [CrossRef] [Google Scholar]
  12. Böhringer, H., & Chon, G. 2015, A&A, 574, L8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Boubert, D., & Everall, A. 2020, MNRAS, 497, 4246 [NASA ADS] [CrossRef] [Google Scholar]
  14. Boubert, D., & Everall, A. 2021, MNRAS, 510, 4626 [Google Scholar]
  15. Brunner, H., Liu, T., Lamer, G., et al. 2022, A&A, 661, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Bulbul, E., Chiu, I. N., Mohr, J. J., et al. 2019, ApJ, 871, 50 [Google Scholar]
  17. Bulbul, E., Liu, A., Pasini, T., et al. 2022, A&A, 661, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Bulbul, E., Liu, A., Kluge, M., et al. 2024, A&A, 685, A106 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Cash, W. 1979, ApJ, 228, 939 [Google Scholar]
  20. Chuang, C.-H., Yepes, G., Kitaura, F.-S., et al. 2019, MNRAS, 487, 48 [NASA ADS] [CrossRef] [Google Scholar]
  21. Clerc, N., & Finoguenov, A. 2023, in Handbook of X-ray and Gamma-ray Astrophysics, eds. C. Bambi, & A. Santangelo, 123 [Google Scholar]
  22. Clerc, N., Ramos-Ceja, M. E., Ridl, J., et al. 2018, A&A, 617, A92 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Comparat, J., Merloni, A., Salvato, M., et al. 2019, MNRAS, 487, 2005 [Google Scholar]
  24. Comparat, J., Eckert, D., Finoguenov, A., et al. 2020, Open J. Astrophys., 3, 13 [Google Scholar]
  25. Comparat, J., Truong, N., Merloni, A., et al. 2022, A&A, 666, A156 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Comparat, J., Luo, W., Merloni, A., et al. 2023, A&A, 673, A122 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Dauser, T., Falkner, S., Lorenz, M., et al. 2019, A&A, 630, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Debackere, S. N. B., Hoekstra, H., Schaye, J., Heitmann, K., & Habib, S. 2022, MNRAS, 515, 3383 [NASA ADS] [CrossRef] [Google Scholar]
  29. de Haan, T., Benson, B. A., Bleem, L. E., et al. 2016, ApJ, 832, 95 [NASA ADS] [CrossRef] [Google Scholar]
  30. Dietrich, J. P., Bocquet, S., Schrabback, T., et al. 2019, MNRAS, 483, 2871 [Google Scholar]
  31. Fernique, P., Boch, T., Donaldson, T., et al. 2014, MOC – HEALPix Multi-Order Coverage Map Version 1.0, IVOA Recommendation 02 June 2014 [Google Scholar]
  32. Finoguenov, A., Rykoff, E., Clerc, N., et al. 2020, A&A, 638, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306 [Google Scholar]
  34. Gallagher, S. C., & Smeenk, C. 2023, in What’s in a Survey? Simulation-Induced Selection Effects in Astronomy, eds. N. Mills Boyd, S. De Baerdemaeker, K. Heng, & V. Matarese (Cham: Springer International Publishing), 207 [Google Scholar]
  35. Garrel, C., Pierre, M., Valageas, P., et al. 2022, A&A, 663, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Georgakakis, A., Aird, J., Schulze, A., et al. 2017, MNRAS, 471, 1976 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ghirardini, V., Eckert, D., Ettori, S., et al. 2019, A&A, 621, A41 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  38. Ghirardini, V., Bulbul, E., Artis, E., et al. 2024, A&A, submitted [arXiv:2402.08458] [Google Scholar]
  39. Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [Google Scholar]
  40. GPy 2012, GPy: A Gaussian Process Framework in Python, http://github.com/SheffieldML/GPy [Google Scholar]
  41. Grandis, S., Klein, M., Mohr, J. J., et al. 2020, MNRAS, 498, 771 [NASA ADS] [CrossRef] [Google Scholar]
  42. Hensman, J., Matthews, A., & Ghahramani, Z. 2015, in Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, eds. G. Lebanon, & S. V. N. Vishwanathan (San Diego, California, USA: PMLR), Proceedings of Machine Learning Research, 38, 351 [Google Scholar]
  43. HI4PI Collaboration (Ben Bekhti, N., et al.) 2016, A&A, 594, A116 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Hoyle, B., Jimenez, R., Verde, L., & Hotchkiss, S. 2012, JCAP, 2012, 009 [CrossRef] [Google Scholar]
  45. Hu, W., & Kravtsov, A. V. 2003, ApJ, 584, 702 [Google Scholar]
  46. Huang, N., Bleem, L. E., Stalder, B., et al. 2020, AJ, 159, 110 [Google Scholar]
  47. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  48. Jasche, J., & Lavaux, G. 2019, A&A, 625, A64 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Kluge, M., Comparat, J., Liu, A., et al. 2024, A&A, in press, https://doi.org/10.1051/0004-6361/202349031 [Google Scholar]
  50. Kong, H., Burleigh, K. J., Ross, A., et al. 2020, MNRAS, 499, 3943 [NASA ADS] [CrossRef] [Google Scholar]
  51. Kostić, A., Nguyen, N.-M., Schmidt, F., & Reinecke, M. 2023, JCAP, 2023, 063 [Google Scholar]
  52. Lehmer, B. D., Xue, Y. Q., Brandt, W. N., et al. 2012, ApJ, 752, 46 [NASA ADS] [CrossRef] [Google Scholar]
  53. Lindholm, V., Finoguenov, A., Comparat, J., et al. 2021, A&A, 646, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Liu, A., Bulbul, E., Ghirardini, V., et al. 2022, A&A, 661, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Liu, A., Bulbul, E., Ramos-Ceja, M. E., et al. 2023, A&A, 670, A96 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Locatelli, N., Ponti, G., Zheng, X., et al. 2024, A&A, 681, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Mantz, A. B. 2019, MNRAS, 485, 4863 [NASA ADS] [CrossRef] [Google Scholar]
  58. Mantz, A. B., von der Linden, A., Allen, S. W., et al. 2015, MNRAS, 446, 2205 [Google Scholar]
  59. Marulli, F., Veropalumbo, A., Sereno, M., et al. 2018, A&A, 620, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Matthews, B. 1975, Biochimica et Biophysica Acta (BBA)– Protein Structure, 405, 442 [CrossRef] [Google Scholar]
  61. Merloni, A., Lamer, G., Liu, T., et al. 2024, A&A, 682, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Moster, B. P., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121 [Google Scholar]
  63. Newcombe, R. G. 1998, Stat. Med., 17, 857 [CrossRef] [Google Scholar]
  64. Pacaud, F., Pierre, M., Refregier, A., et al. 2006, MNRAS, 372, 578 [NASA ADS] [CrossRef] [Google Scholar]
  65. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  66. Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Ponti, G., Zheng, X., Locatelli, N., et al. 2023, A&A, 674, A195 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  68. Predehl, P., Sunyaev, R. A., Becker, W., et al. 2020, Nature, 588, 227 [CrossRef] [Google Scholar]
  69. Predehl, P., Andritschke, R., Arefiev, V., et al. 2021, A&A, 647, A1 [EDP Sciences] [Google Scholar]
  70. Rasmussen, C. E., & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning (MIT Press) [Google Scholar]
  71. Rix, H.-W., Hogg, D. W., Boubert, D., et al. 2021, AJ, 162, 142 [NASA ADS] [CrossRef] [Google Scholar]
  72. Salvato, M., Buchner, J., Budavári, T., et al. 2018, MNRAS, 473, 4937 [Google Scholar]
  73. Schmidt, F. 2021a, JCAP, 2021, 033 [CrossRef] [Google Scholar]
  74. Schmidt, F. 2021b, JCAP, 2021, 032 [CrossRef] [Google Scholar]
  75. Schneider, P. C., Freund, S., Czesla, S., et al. 2022, A&A, 661, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Seppi, R., Comparat, J., Bulbul, E., et al. 2022, A&A, 665, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Seppi, R., Comparat, J., Ghirardini, V., et al. 2024, A&A, 686, A196 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Suchyta, E., Huff, E. M., Aleksić, J., et al. 2016, MNRAS, 457, 786 [Google Scholar]
  79. Tinker, J., Kravtsov, A. V., Klypin, A., et al. 2008, ApJ, 688, 709 [Google Scholar]
  80. Turner, D. J., Giles, P. A., Romer, A. K., et al. 2022, MNRAS, 517, 657 [NASA ADS] [CrossRef] [Google Scholar]
  81. Vanderlinde, K., Crawford, T. M., de Haan, T., et al. 2010, ApJ, 722, 1180 [CrossRef] [Google Scholar]
  82. Veronica, A., Reiprich, T. H., Pacaud, F., et al. 2024, A&A, 681, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Vollset, S. E. 1993, Stat. Med., 12, 809 [CrossRef] [Google Scholar]
  84. Wilson, E. B. 1927, J. Am. Stat. Assoc., 22, 209 [CrossRef] [Google Scholar]
  85. Zeiler, M. D. 2012, arXiv e-prints [arXiv:1212.5701] [Google Scholar]
  86. Zhang, Y., Comparat, J., Ponti, G., et al. 2024, A&A, submitted [arXiv:2401.17308] [Google Scholar]
  87. Zheng, X., Ponti, G., Locatelli, N., et al. 2024a, A&A, submitted [arXiv:2401.17310] [Google Scholar]
  88. Zheng, X., Ponti, G., Freyberg, M., et al. 2024b, A&A, 681, A77 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  89. Zonca, A., Singer, L., Lenz, D., et al. 2019, J. Open Source Softw., 4, 1298 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.