EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 695 (March 2025) A&A, 695 (2025) A284 References
Open Access
Issue
A&A
Volume 695, March 2025
Article Number A284
Number of page(s) 26
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202453111
Published online 03 April 2025
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, software available from tensorflow.org [Google Scholar]
  2. Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543 [Google Scholar]
  3. Abdalla, F. B., Banerji, M., Lahav, O., & Rashkov, V. 2011, MNRAS, 417, 1891 [NASA ADS] [CrossRef] [Google Scholar]
  4. Abdurro’uf, Lin, Y.-T., Wu, P.-F., & Akiyama, M. 2021, ApJS, 254, 15 [CrossRef] [Google Scholar]
  5. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022a, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  6. Abdurro’uf, Lin, Y.-T., Hirashita, H., et al. 2022b, ApJ, 935, 98 [CrossRef] [Google Scholar]
  7. Abdurro’uf, Coe, D., Jung, I., et al. 2023, ApJ, 945, 117 [CrossRef] [Google Scholar]
  8. Acquaviva, V. 2015, MNRAS, 456, 1618 [Google Scholar]
  9. Alsing, J., Peiris, H., Mortlock, D., Leja, J., & Leistedt, B. 2023, ApJS, 264, 29 [NASA ADS] [CrossRef] [Google Scholar]
  10. Alsing, J., Thorp, S., Deger, S., et al. 2024, ApJS, 274, 12 [NASA ADS] [CrossRef] [Google Scholar]
  11. Baes, M., & Dejonghe, H. 2001, MNRAS, 326, 722 [NASA ADS] [CrossRef] [Google Scholar]
  12. Baes, M., Verstappen, J., De Looze, I., et al. 2011, ApJS, 196, 22 [Google Scholar]
  13. Baes, M., Gebek, A., Trčka, A., et al. 2024, A&A, 683, A181 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Baker, W. M., Maiolino, R., Belfiore, F., et al. 2022, MNRAS, 519, 1149 [NASA ADS] [CrossRef] [Google Scholar]
  15. Baron, D. 2019, ArXiv e-prints [arXiv:1904.07248] [Google Scholar]
  16. Bell, E. F., & de Jong, R. S. 2001, ApJ, 550, 212 [Google Scholar]
  17. Bellstedt, S., Robotham, A. S. G., Driver, S. P., et al. 2021, MNRAS, 503, 3309 [NASA ADS] [CrossRef] [Google Scholar]
  18. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  19. Brescia, M., Cavuoti, S., D’Abrusco, R., Longo, G., & Mercurio, A. 2013, ApJ, 772, 140 [NASA ADS] [CrossRef] [Google Scholar]
  20. Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 [Google Scholar]
  21. Bruzual, G., & Charlot, S. 1993, ApJ, 405, 538 [Google Scholar]
  22. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  23. Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7 [Google Scholar]
  24. Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682 [NASA ADS] [CrossRef] [Google Scholar]
  25. Camps, P., & Baes, M. 2018, ApJ, 861, 80 [NASA ADS] [CrossRef] [Google Scholar]
  26. Camps, P., & Baes, M. 2020, Astron. Comput., 31, 100381 [NASA ADS] [CrossRef] [Google Scholar]
  27. Camps, P., Misselt, K., Bianchi, S., et al. 2015, A&A, 580, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Carliles, S., Budavári, T., Heinis, S., Priebe, C., & Szalay, A. S. 2010, ApJ, 712, 511 [NASA ADS] [CrossRef] [Google Scholar]
  29. Cavuoti, S., Brescia, M., Longo, G., & Mercurio, A. 2012, A&A, 546, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Chabrier, G. 2003, PASP, 115, 763 [Google Scholar]
  31. Chamba, N., Trujillo, I., & Knapen, J. H. 2022, A&A, 667, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  32. Charlot, S., & Bruzual, G. 1991, ApJ, 367, 126 [Google Scholar]
  33. Charlot, S., & Fall, S. M. 2000, ApJ, 539, 718 [Google Scholar]
  34. Chicco, D., Warrens, M., & Jurman, G. 2021, PeerJ Comput. Sci., 486, 5104 [Google Scholar]
  35. Collister, A. A., & Lahav, O. 2004, PASP, 116, 345 [NASA ADS] [CrossRef] [Google Scholar]
  36. Conroy, C. 2013, ARA&A, 51, 393 [NASA ADS] [CrossRef] [Google Scholar]
  37. Cropper, M., Pottinger, S., Azzollini, R., et al. 2018, in Space Telescopes and Instrumentation 2018: Optical, Infrared, and Millimeter Wave, eds. M. Lystrup, H. A. MacEwen, G. G. Fazio, et al., SPIE Conf. Ser., 10698, 1069828 [Google Scholar]
  38. D’Abrusco, R., Staiano, A., Longo, G., et al. 2007, ApJ, 663, 752 [Google Scholar]
  39. Dahl, G. E., Sainath, T. N., & Hinton, G. E. 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 8609 [Google Scholar]
  40. Dale, D. A., & Helou, G. 2002, ApJ, 576, 159 [Google Scholar]
  41. Decleir, M., De Looze, I., Boquien, M., et al. 2019, MNRAS, 486, 743 [NASA ADS] [CrossRef] [Google Scholar]
  42. Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, MNRAS, 486, 1377 [Google Scholar]
  43. D’Isanto, A., & Polsterer, K. L. 2018, A&A, 609, A111 [Google Scholar]
  44. Ditrani, F. R., Longhetti, M., La Barbera, F., et al. 2023, A&A, 677, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Dobbels, W., & Baes, M. 2021, A&A, 655, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Dobbels, W., Baes, M., Viaene, S., et al. 2020, A&A, 634, A57 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  47. Draine, B. T. 2003, ARA&A, 41, 241 [NASA ADS] [CrossRef] [Google Scholar]
  48. Duchi, J., Hazan, E., & Singer, Y. 2011, J. Mach. Learn. Res., 12, 2121 [Google Scholar]
  49. Euclid Collaboration (Desprez, G., et al.) 2020, A&A, 644, A31 [EDP Sciences] [Google Scholar]
  50. Euclid Collaboration (Bretonnière, H., et al.) 2022, A&A, 657, A90 [CrossRef] [EDP Sciences] [Google Scholar]
  51. Euclid Collaboration (Scaramella, R., et al.) 2022, A&A, 662, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Euclid Collaboration (Schirmer, M., et al.) 2022, A&A, 662, A92 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Euclid Collaboration (Bisigello, L., et al.) 2023, MNRAS, 520, 3529 [NASA ADS] [CrossRef] [Google Scholar]
  54. Euclid Collaboration (Bretonnière, H., et al.) 2023, A&A, 671, A102 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Euclid Collaboration (Humphrey, A., et al.) 2023, A&A, 671, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  56. Euclid Collaboration (Merlin, E., et al.) 2023, A&A, 671, A101 [Google Scholar]
  57. Euclid Collaboration (Aussel, B., et al.) 2024, A&A, 689, A274 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Euclid Collaboration (Enia, A., et al.) 2024, A&A, 691, A175 [NASA ADS] [Google Scholar]
  59. Euclid Collaboration (Leuzzi, L., et al.) 2024, A&A, 681, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Euclid Collaboration (Jahnke, K., et al.) 2025, A&A, in press, https://doi.org/10.1051/0004-6361/202450786 [Google Scholar]
  61. Euclid Collaboration (Mellier, Y., et al.) 2025, A&A, in press, https://doi.org/10.1051/0004-6361/202450810 [Google Scholar]
  62. Faber, S. M. 1973, ApJ, 179, 731 [NASA ADS] [CrossRef] [Google Scholar]
  63. Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41 [Google Scholar]
  64. Galliano, F., Nersesian, A., Bianchi, S., et al. 2021, A&A, 649, A18 [EDP Sciences] [Google Scholar]
  65. Gao, Y., Bao, M., Yuan, Q., et al. 2018, ApJ, 869, 15 [NASA ADS] [CrossRef] [Google Scholar]
  66. Genel, S., Vogelsberger, M., Springel, V., et al. 2014, MNRAS, 445, 175 [Google Scholar]
  67. Gilda, S., Lower, S., & Narayanan, D. 2021, ApJ, 916, 43 [NASA ADS] [CrossRef] [Google Scholar]
  68. Glorot, X., Bordes, A., & Bengio, Y. 2011, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, eds. G. Gordon, D. Dunson, & M. Dudík (Fort Lauderdale, FL, USA: PMLR), Proc. Mach. Learn. Res., 15, 315 [Google Scholar]
  69. González Delgado, R. M., Cid Fernandes, R., García-Benito, R., et al. 2014, ApJ, 791, L16 [CrossRef] [Google Scholar]
  70. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press), http://www.deeplearningbook.org [Google Scholar]
  71. Hastie, T., Tibshirani, R., & Friedman, J. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Science& Business Media) [Google Scholar]
  72. Hildebrandt, H., Arnouts, S., Capak, P., et al. 2010, A&A, 523, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Höhle, J., & Höhle, M. 2009, ISPRS J. Photogramm. Remote Sens., 64, 398 [CrossRef] [Google Scholar]
  74. Hüllermeier, E., & Waegeman, W. 2021, Mach. Learn., 110, 457 [CrossRef] [Google Scholar]
  75. Iglesias-Navarro, P., Huertas-Company, M., Martín-Navarro, I., Knapen, J. H., & Pernet, E. 2024, A&A, 689, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  77. James, G., Witten, D., Hasatie, T., Tibshirani, R., & Taylor, J. 2023, An Introduction to Statistical Learning: with Applications in Python (Springer) [Google Scholar]
  78. Jost, L. 2006, Oikos, 113, 363 [NASA ADS] [Google Scholar]
  79. Kauffmann, G., Heckman, T. M., White, D. M. S., et al. 2003, MNRAS, 341, 33 [CrossRef] [Google Scholar]
  80. Kendall, A., & Gal, Y. 2017, in Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook, NY, USA: Curran Associates Inc.), 5580 [Google Scholar]
  81. Kingma, D. P., & Ba, J. 2015, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, eds. Y. Bengio, & Y. LeCun [Google Scholar]
  82. Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2017, Commun. ACM, 60, 84 [CrossRef] [Google Scholar]
  83. Lagache, G., Dole, H., Puget, J. L., et al. 2004, ApJS, 154, 112 [NASA ADS] [CrossRef] [Google Scholar]
  84. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
  85. Law, K.-H., Gordon, K. D., & Misselt, K. A. 2018, ApJS, 236, 32 [NASA ADS] [CrossRef] [Google Scholar]
  86. Leistedt, B., Alsing, J., Peiris, H., Mortlock, D., & Leja, J. 2023, ApJS, 264, 23 [NASA ADS] [CrossRef] [Google Scholar]
  87. Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170 [NASA ADS] [CrossRef] [Google Scholar]
  88. Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019, ApJ, 876, 3 [Google Scholar]
  89. Lequeux, J., Peimbert, M., Rayo, J. F., Serrano, A., & Torres-Peimbert, S. 1979, A&A, 80, 155 [Google Scholar]
  90. Lo Faro, B., Buat, V., Roehlly, Y., et al. 2017, MNRAS, 472, 1372 [NASA ADS] [CrossRef] [Google Scholar]
  91. Longo, L., Brcic, M., Cabitza, F., et al. 2024, Inf. Fusion, 106, 102301 [Google Scholar]
  92. Lovell, C. C., Acquaviva, V., Thomas, P. A., et al. 2019, MNRAS, 490, 5503 [NASA ADS] [CrossRef] [Google Scholar]
  93. Lu, Y., Wechsler, R. H., Somerville, R. S., et al. 2014, ApJ, 795, 123 [CrossRef] [Google Scholar]
  94. Luhman, M. L., Satyapal, S., Fischer, J., et al. 2003, ApJ, 594, 758 [Google Scholar]
  95. Ly, C., Malkan, M. A., Rigby, J. R., & Nagao, T. 2016, ApJ, 828, 67 [NASA ADS] [CrossRef] [Google Scholar]
  96. Maiolino, R., & Mannucci, F. 2019, A&ARv, 27, 3 [Google Scholar]
  97. Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113 [NASA ADS] [Google Scholar]
  98. Matteucci, F. 2008, in Massive Stars as Cosmic Engines, eds. F. Bresolin, P. A. Crowther, & J. Puls, 250, 391 [NASA ADS] [Google Scholar]
  99. Murphy, K. 2012, Machine Learning: A Probabilistic Perspective (MIT Press) [Google Scholar]
  100. Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206 [Google Scholar]
  101. Nair, V., & Hinton, G. E. 2010, in Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10 (Madison, WI, USA: Omnipress), 807 [Google Scholar]
  102. Nelson, D., Springel, V., Pillepich, A., et al. 2019a, Comput. Astrophys. Cosmol., 6, 2 [Google Scholar]
  103. Nelson, D., Pillepich, A., Springel, V., et al. 2019b, MNRAS, 490, 3234 [Google Scholar]
  104. Nersesian, A., Xilouris, E. M., Bianchi, S., et al. 2019, A&A, 624, A80 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  105. Nersesian, A., van der Wel, A., Gallazzi, A., et al. 2024, A&A, 681, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  106. Neumann, J., Thomas, D., Maraston, C., et al. 2021, MNRAS, 508, 4844 [NASA ADS] [CrossRef] [Google Scholar]
  107. Pacifici, C., Iyer, K. G., Mobasher, B., et al. 2023, ApJ, 944, 141 [NASA ADS] [CrossRef] [Google Scholar]
  108. Pearson, K. 1895, Proc. R. Soc. Lond. Ser. I, 58, 240 [Google Scholar]
  109. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  110. Pellerin, A., & Finkelstein, S. L. 2010, Proc. Int. Astron. Union, 262, 283 [Google Scholar]
  111. Pillepich, A., Nelson, D., Hernquist, L., et al. 2017, MNRAS, 475, 648 [Google Scholar]
  112. Pillepich, A., Springel, V., Nelson, D., et al. 2018, MNRAS, 473, 4077 [Google Scholar]
  113. Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196 [Google Scholar]
  114. Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  115. Poggianti, B. M., & Barbaro, G. 1997, A&A, 325, 1025 [NASA ADS] [Google Scholar]
  116. Popesso, P., Concas, A., Cresci, G., et al. 2023, MNRAS, 519, 1526 [Google Scholar]
  117. Rosales-Ortega, F. F., Sánchez, S. F., Iglesias-Páramo, J., et al. 2012, ApJ, 756, L31 [NASA ADS] [CrossRef] [Google Scholar]
  118. Sadeh, I., Abdalla, F. B., & Lahav, O. 2016, PASP, 128, 104502 [NASA ADS] [CrossRef] [Google Scholar]
  119. Salim, S., & Narayanan, D. 2020, ARA&A, 58, 529 [NASA ADS] [CrossRef] [Google Scholar]
  120. Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11 [Google Scholar]
  121. Salucci, P. 2019, A&ARv, 27, 2 [NASA ADS] [CrossRef] [Google Scholar]
  122. Sánchez, S. F. 2020, ARA&A, 58, 99 [Google Scholar]
  123. Sánchez, S. F., Kennicutt, R. C., Gil de Paz, A., et al. 2012, A&A, 538, A8 [Google Scholar]
  124. Sánchez, S. F., Rosales-Ortega, F. F., Jungwiert, B., et al. 2013, A&A, 554, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  125. Sanders, N. E., Levesque, E. M., & Soderberg, A. M. 2013, ApJ, 775, 125 [Google Scholar]
  126. Schmidt, M. 1959, ApJ, 129, 243 [NASA ADS] [CrossRef] [Google Scholar]
  127. Siebenmorgen, R., & Krügel, E. 2007, A&A, 461, 445 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  128. Simet, M., Chartab, N., Lu, Y., & Mobasher, B. 2021, ApJ, 908, 47 [NASA ADS] [CrossRef] [Google Scholar]
  129. Sorba, R., & Sawicki, M. 2015, MNRAS, 452, 235 [NASA ADS] [CrossRef] [Google Scholar]
  130. Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15 [Google Scholar]
  131. Springel, V. 2010, MNRAS, 401, 791 [Google Scholar]
  132. Springel, V., Pakmor, R., Pillepich, A., et al. 2017, MNRAS, 475, 676 [Google Scholar]
  133. Steinacker, J., Baes, M., & Gordon, K. D. 2013, ARA&A, 51, 63 [CrossRef] [Google Scholar]
  134. Stensbo-Smidt, K., Gieseke, F., Igel, C., Zirm, A., & Steenstrup Pedersen, K. 2017, MNRAS, 464, 2577 [NASA ADS] [CrossRef] [Google Scholar]
  135. Surana, S., Wadadekar, Y., Bait, O., & Bhosale, H. 2020, MNRAS, 493, 4808 [NASA ADS] [CrossRef] [Google Scholar]
  136. Tagliaferri, R., Longo, G., Andreon, S., et al. 2003, Lect. Notes Comput. Sci., 2859, 226 [NASA ADS] [CrossRef] [Google Scholar]
  137. Tammes, P. M. L. 1930, Recl. Trav. Botaniques Neerl., 27, 1 [Google Scholar]
  138. Thorne, J. E., Robotham, A. S. G., Bellstedt, S., et al. 2022, MNRAS, 517, 6035 [NASA ADS] [CrossRef] [Google Scholar]
  139. Torrey, P., Vogelsberger, M., Marinacci, F., et al. 2019, MNRAS, 484, 5587 [NASA ADS] [Google Scholar]
  140. Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898 [Google Scholar]
  141. Viaene, S., Fritz, J., Baes, M., et al. 2014, A&A, 567, A71 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  142. Vilone, G., & Longo, L. 2021, Mach. Learn. Knowl. Extr., 3, 615 [CrossRef] [Google Scholar]
  143. Vogelsberger, M., Genel, S., Springel, V., et al. 2014, MNRAS, 444, 1518 [Google Scholar]
  144. Walcher, J., Groves, B., Budavári, T., & Dale, D. 2011, Ap&SS, 331, 1 [NASA ADS] [CrossRef] [Google Scholar]
  145. Watkins, A. E., Salo, H., Laurikainen, E., et al. 2022, A&A, 660, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  146. Webb, K., Balogh, M. L., Leja, J., et al. 2020, MNRAS, 498, 5317 [NASA ADS] [CrossRef] [Google Scholar]
  147. Weinberger, R., Springel, V., Hernquist, L., et al. 2016, MNRAS, 465, 3291 [Google Scholar]
  148. Whitaker, K. E., van Dokkum, P. G., Brammer, G., & Franx, M. 2012, ApJ, 754, L29 [Google Scholar]
  149. Wright, S. 1921, J. Agric. Res., 20, 557 [Google Scholar]
  150. Zahid, H. J., Kudritzki, R.-P., Conroy, C., Andrews, B., & Ho, I. T. 2017, ApJ, 847, 18 [Google Scholar]
  151. Zanisi, L., Huertas-Company, M., Lanusse, F., et al. 2021, MNRAS, 501, 4359 [NASA ADS] [CrossRef] [Google Scholar]
  152. Zeiler, M., Ranzato, M., Monga, R., et al. 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 3517 [Google Scholar]
  153. Zibetti, S., & Gallazzi, A. R. 2022, MNRAS, 512, 1415 [NASA ADS] [CrossRef] [Google Scholar]
  154. Zibetti, S., Charlot, S., & Rix, H.-W. 2009, MNRAS, 400, 1181 [NASA ADS] [CrossRef] [Google Scholar]
  155. Zibetti, S., Gallazzi, A. R., Hirschmann, M., et al. 2020, MNRAS, 491, 3562 [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.