EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 687 (July 2024) A&A, 687 (2024) A45 References
Open Access
Issue
A&A
Volume 687, July 2024
Article Number A45
Number of page(s) 16
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202449532
Published online 25 June 2024
  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. Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. 2018, MNRAS, 479, 415 [NASA ADS] [CrossRef] [Google Scholar]
  3. Barton, E. J., Geller, M. J., & Kenyon, S. J. 2000, ApJ, 530, 660 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bickley, R. W., Bottrell, C., Hani, M. H., et al. 2021, MNRAS, 504, 372 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bickley, R. W., Ellison, S. L., Patton, D. R., et al. 2022, MNRAS, 514, 3294 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bottrell, C., Hani, M. H., Teimoorinia, H., et al. 2019, MNRAS, 490, 5390 [NASA ADS] [CrossRef] [Google Scholar]
  7. Boylan-Kolchin, M., Springel, V., White, S. D. M., Jenkins, A., & Lemson, G. 2009, MNRAS, 398, 1150 [Google Scholar]
  8. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  9. Byrne-Mamahit, S., Hani, M. H., Ellison, S. L., Quai, S., & Patton, D. R. 2023, MNRAS, 519, 4966 [NASA ADS] [CrossRef] [Google Scholar]
  10. Chen, M., Wu, K., Ni, B., et al. 2021, in Advances in Neural Information Processing Systems, eds. M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. W. Vaughan (Curran Associates, Inc.), 34, 8714 [Google Scholar]
  11. Ćiprijanović, A., Snyder, G. F., Nord, B., & Peek, J. E. G. 2020, Astron. Comput., 32, 100390 [CrossRef] [Google Scholar]
  12. Conselice, C. J. 2014, ARA&A, 52, 291 [CrossRef] [Google Scholar]
  13. Conselice, C. J., Bershady, M. A., & Jangren, A. 2000, ApJ, 529, 886 [NASA ADS] [CrossRef] [Google Scholar]
  14. Conselice, C. J., Bershady, M. A., Dickinson, M., & Papovich, C. 2003, AJ, 126, 1183 [CrossRef] [Google Scholar]
  15. de Jong, J. T. A., Kuijken, K., Applegate, D., et al. 2013a, The Messenger, 154, 44 [NASA ADS] [Google Scholar]
  16. de Jong, J. T. A., Verdoes Kleijn, G. A., Kuijken, K. H., & Valentijn, E. A. 2013b, Exp. Astron., 35, 25 [Google Scholar]
  17. De Propris, R., Liske, J., Driver, S. P., Allen, P. D., & Cross, N. J. G. 2005, AJ, 130, 1516 [NASA ADS] [CrossRef] [Google Scholar]
  18. Desmons, A., Brough, S., Martínez-Lombilla, C., et al. 2023, MNRAS, 523, 4381 [CrossRef] [Google Scholar]
  19. Dubois, Y., Pichon, C., Welker, C., et al. 2014, MNRAS, 444, 1453 [Google Scholar]
  20. Duncan, K., Conselice, C. J., Mundy, C., et al. 2019, ApJ, 876, 110 [NASA ADS] [CrossRef] [Google Scholar]
  21. Ellison, S. L., Mendel, J. T., Patton, D. R., & Scudder, J. M. 2013, MNRAS, 435, 3627 [CrossRef] [Google Scholar]
  22. Ferreira, L., Conselice, C. J., Duncan, K., et al. 2020, ApJ, 895, 115 [NASA ADS] [CrossRef] [Google Scholar]
  23. Fisher, R. A. 1936, Ann. Eugenics, 7, 179 [CrossRef] [Google Scholar]
  24. Gao, F., Wang, L., Pearson, W. J., et al. 2020, A&A, 637, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Goldberger, J., Hinton, G. E., Roweis, S., & Salakhutdinov, R. R. 2004, in Advances in Neural Information Processing Systems, eds. L. Saul, Y. Weiss, & L. Bottou (MIT Press), 17 [Google Scholar]
  26. Guzmán-Ortega, A., Rodriguez-Gomez, V., Snyder, G. F., Chamberlain, K., & Hernquist, L. 2023, MNRAS, 519, 4920 [CrossRef] [Google Scholar]
  27. Halko, N., Martinsson, P. G., & Tropp, J. A. 2011, SIAM Rev., 53, 217 [CrossRef] [Google Scholar]
  28. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in 2016 IEEEConference on Computer Vision and Pattern Recognition (CVPR), 770 [CrossRef] [Google Scholar]
  29. Holwerda, B. W., Kelvin, L., Baldry, I., et al. 2019, AJ, 158, 103 [NASA ADS] [CrossRef] [Google Scholar]
  30. Hopkins, P. F., Wetzel, A., Kereš, D., et al. 2018, MNRAS, 480, 800 [NASA ADS] [CrossRef] [Google Scholar]
  31. Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8 [NASA ADS] [CrossRef] [Google Scholar]
  32. Kiefer, J., & Wolfowitz, J. 1952, Ann. Math. Stat., 23, 462 [CrossRef] [Google Scholar]
  33. 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]
  34. Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742 [NASA ADS] [CrossRef] [Google Scholar]
  35. Koppula, S., Bapst, V., Huertas-Company, M., et al. 2021, arXiv e-prints [arXiv:2102.05182] [Google Scholar]
  36. Li, P., Hastie, T. J., & Church, K. W. 2006, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’06 (New York, USA: Association for Computing Machinery), 287 [CrossRef] [Google Scholar]
  37. Lintott, C. J., Schawinski, K., Slosar, A., et al. 2008, MNRAS, 389, 1179 [NASA ADS] [CrossRef] [Google Scholar]
  38. Liu, Z., Lin, Y., Cao, Y., et al. 2021, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) [Google Scholar]
  39. Lotz, J. M., Primack, J., & Madau, P. 2004, AJ, 128, 163 [NASA ADS] [CrossRef] [Google Scholar]
  40. Lotz, J. M., Davis, M., Faber, S. M., et al. 2008, ApJ, 672, 177 [NASA ADS] [CrossRef] [Google Scholar]
  41. Margalef-Bentabol, B., Wang, L., La Marca, A., et al. 2024, arXiv e-prints [arXiv:2403.15118] [Google Scholar]
  42. Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113 [NASA ADS] [Google Scholar]
  43. McInnes, L., Healy, J., & Melville, J. 2018, arXiv e-prints [arXiv:1802.03426] [Google Scholar]
  44. Moreno, J., Torrey, P., Ellison, S. L., et al. 2019, MNRAS, 485, 1320 [NASA ADS] [CrossRef] [Google Scholar]
  45. Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206 [Google Scholar]
  46. 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]
  47. Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624 [Google Scholar]
  48. Nelson, D., Springel, V., Pillepich, A., et al. 2019, Comput. Astrophys. Cosmol., 6, 2 [Google Scholar]
  49. Pearson, W. J., Wang, L., Alpaslan, M., et al. 2019, A&A, 631, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  50. Pearson, W. J., Suelves, L. E., Ho, S. C. C., et al. 2022, A&A, 661, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  52. Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648 [Google Scholar]
  53. Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Robbins, H., & Monro, S. 1951, Ann. Math. Stat., 22, 400 [CrossRef] [Google Scholar]
  55. Robotham, A. S. G., Driver, S. P., Davies, L. J. M., et al. 2014, MNRAS, 444, 3986 [NASA ADS] [CrossRef] [Google Scholar]
  56. Rodrigues, M., Puech, M., Flores, H., Hammer, F., & Pirzkal, N. 2018, MNRAS, 475, 5133 [NASA ADS] [CrossRef] [Google Scholar]
  57. Rodriguez-Gomez, V., Genel, S., Vogelsberger, M., et al. 2015, MNRAS, 449, 49 [Google Scholar]
  58. Rodriguez-Gomez, V., Snyder, G. F., Lotz, J. M., et al. 2019, MNRAS, 483, 4140 [NASA ADS] [CrossRef] [Google Scholar]
  59. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comput. Vis., 115, 211 [Google Scholar]
  60. Sanders, D. B., & Mirabel, I. F. 1996, ARA&A, 34, 749 [Google Scholar]
  61. Silva, A., Marchesini, D., Silverman, J. D., et al. 2018, ApJ, 868, 46 [NASA ADS] [CrossRef] [Google Scholar]
  62. Silva, A., Marchesini, D., Silverman, J. D., et al. 2021, ApJ, 909, 124 [Google Scholar]
  63. Snyder, G. F., Lotz, J., Moody, C., et al. 2015, MNRAS, 451, 4290 [NASA ADS] [CrossRef] [Google Scholar]
  64. Snyder, G. F., Rodriguez-Gomez, V., Lotz, J. M., et al. 2019, MNRAS, 486, 3702 [NASA ADS] [CrossRef] [Google Scholar]
  65. Somerville, R. S., & Davé, R. 2015, ARA&A, 53, 51 [Google Scholar]
  66. Springel, V., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629 [Google Scholar]
  67. Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676 [Google Scholar]
  68. Steffen, J. L., Fu, H., Brownstein, J. R., et al. 2023, ApJ, 942, 107 [NASA ADS] [CrossRef] [Google Scholar]
  69. Suelves, L. E., Pearson, W. J., & Pollo, A. 2023, A&A, 669, A141 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Taranu, D. S., Dubinski, J., & Yee, H. K. C. 2013, ApJ, 778, 61 [NASA ADS] [CrossRef] [Google Scholar]
  71. Tenenbaum, J. B., de Silva, V., & Langford, J. C. 2000, Science, 290, 2319 [CrossRef] [PubMed] [Google Scholar]
  72. Walmsley, M., Ferguson, A. M. N., Mann, R. G., & Lintott, C. J. 2019, MNRAS, 483, 2968 [NASA ADS] [CrossRef] [Google Scholar]
  73. Wang, L., Pearson, W. J., & Rodriguez-Gomez, V. 2020, A&A, 644, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Weston, M. E., McIntosh, D. H., Brodwin, M., et al. 2017, MNRAS, 464, 3882 [NASA ADS] [CrossRef] [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.