Free Access
Issue
A&A
Volume 649, May 2021
Article Number A81
Number of page(s) 17
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202039684
Published online 13 May 2021
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, tensorflow.org [Google Scholar]
  2. Abolfathi, B., Aguado, D. S., Aguilar, G., et al. 2018, ApJS, 235, 42 [NASA ADS] [CrossRef] [Google Scholar]
  3. Asgari, M., Lin, C.-A., Joachimi, B., et al. 2021, A&A, 645, A104 [CrossRef] [EDP Sciences] [Google Scholar]
  4. Assef, R. J., Stern, D., Noirot, G., et al. 2018, ApJS, 234, 23 [NASA ADS] [CrossRef] [Google Scholar]
  5. Benítez, N. 2000, ApJ, 536, 571 [Google Scholar]
  6. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Bishop, C. M. 2006, Pattern Recognition and Machine Learning, Information Science and Statistics (New York, NY: Springer) softcover published in 2016 [Google Scholar]
  8. Bovy, J., Hennawi, J. F., Hogg, D. W., et al. 2011, ApJ, 729, 141 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bovy, J., Myers, A. D., Hennawi, J. F., et al. 2012, ApJ, 749, 41 [NASA ADS] [CrossRef] [Google Scholar]
  10. Breiman, L. 2001, Mach. Learn., 45, 5 [CrossRef] [Google Scholar]
  11. Brescia, M., Cavuoti, S., D’Abrusco, R., Longo, G., & Mercurio, A. 2013, ApJ, 772, 140 [NASA ADS] [CrossRef] [Google Scholar]
  12. Brescia, M., Cavuoti, S., & Longo, G. 2015, MNRAS, 450, 3893 [NASA ADS] [CrossRef] [Google Scholar]
  13. Calistro Rivera, G., Lusso, E., Hennawi, J. F., & Hogg, D. W. 2016, ApJ, 833, 98 [NASA ADS] [CrossRef] [Google Scholar]
  14. Capaccioli, M., Schipani, P., de Paris, G., et al. 2012, Science from the Next Generation Imaging and Spectroscopic Surveys, 1 [Google Scholar]
  15. Carrasco, D., Barrientos, L. F., Pichara, K., et al. 2015, A&A, 584, A44 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Chen, T., & Guestrin, C. 2016, Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (New York, NY, USA: ACM), 785 [Google Scholar]
  17. Chollet, F. 2015, keras, https://github.com/fchollet/keras [Google Scholar]
  18. Ciesla, L., Charmandaris, V., Georgakakis, A., et al. 2015, A&A, 576, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Clarke, A. O., Scaife, A. M. M., Greenhalgh, R., & Griguta, V. 2020, A&A, 639, A84 [CrossRef] [EDP Sciences] [Google Scholar]
  20. Croom, S. M., Smith, R. J., Boyle, B. J., et al. 2004, MNRAS, 349, 1397 [NASA ADS] [CrossRef] [Google Scholar]
  21. Croom, S. M., Richards, G. T., Shanks, T., et al. 2009, MNRAS, 392, 19 [NASA ADS] [CrossRef] [Google Scholar]
  22. Cuoco, A., Bilicki, M., Xia, J.-Q., & Branchini, E. 2017, ApJS, 232, 10 [NASA ADS] [CrossRef] [Google Scholar]
  23. Curran, S. J. 2020, MNRAS, 493, L70 [NASA ADS] [CrossRef] [Google Scholar]
  24. de Jong, J. T. A., Kuijken, K., Applegate, D., et al. 2013, Messenger, 154, 44 [Google Scholar]
  25. de Jong, J. T. A., Verdoes Kleijn, G. A., Boxhoorn, D. R., et al. 2015, A&A, 582, A62 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. de Jong, J. T. A., Verdoes Kleijn, G. A., Erben, T., et al. 2017, A&A, 604, A134 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. de Jong, R. S., Agertz, O., Berbel, A. A., et al. 2019, Messenger, 175, 3 [Google Scholar]
  28. DESI Collaboration (Aghamousa, A., et al.) 2016, ArXiv e-prints [arXiv:1611.00036] [Google Scholar]
  29. DiPompeo, M. A., Myers, A. D., Hickox, R. C., Geach, J. E., & Hainline, K. N. 2014, MNRAS, 442, 3443 [NASA ADS] [CrossRef] [Google Scholar]
  30. DiPompeo, M. A., Bovy, J., Myers, A. D., & Lang, D. 2015, MNRAS, 452, 3124 [NASA ADS] [CrossRef] [Google Scholar]
  31. DiPompeo, M. A., Hickox, R. C., & Myers, A. D. 2016, MNRAS, 456, 924 [NASA ADS] [CrossRef] [Google Scholar]
  32. DiPompeo, M. A., Hickox, R. C., Eftekharzadeh, S., & Myers, A. D. 2017, MNRAS, 469, 4630 [NASA ADS] [CrossRef] [Google Scholar]
  33. D’Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K. L. 2018, A&A, 616, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Edelson, R., & Malkan, M. 2012, ApJ, 751, 52 [NASA ADS] [CrossRef] [Google Scholar]
  35. Edge, A., Sutherland, W., Kuijken, K., et al. 2013, Messenger, 154, 32 [Google Scholar]
  36. Eftekharzadeh, S., Myers, A. D., White, M., et al. 2015, MNRAS, 453, 2779 [NASA ADS] [CrossRef] [Google Scholar]
  37. Fan, X. 2006, New Astron. Rev., 50, 665 [Google Scholar]
  38. Fotopoulou, S., & Paltani, S. 2018, A&A, 619, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Fotopoulou, S., Pacaud, F., Paltani, S., et al. 2016, A&A, 592, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Gaia Collaboration (Brown, A. G. A., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Hausen, R., & Robertson, B. E. 2020, ApJS, 248, 20 [CrossRef] [Google Scholar]
  42. Haykin, S. 1998, Neural Networks: A Comprehensive Foundation, 2nd edn. (Upper Saddle River, NJ, USA: Prentice Hall PTR) [Google Scholar]
  43. Heintz, K. E., Fynbo, J. P. U., Ledoux, C., et al. 2018, A&A, 615, A43 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Heymans, C., Tröster, T., Asgari, M., et al. 2021, A&A, 646, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Hildebrandt, H., Köhlinger, F., van den Busch, J. L., et al. 2020, A&A, 633, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19 [Google Scholar]
  47. Ho, S., Agarwal, N., Myers, A. D., et al. 2015, JCAP, 5, 040 [NASA ADS] [CrossRef] [Google Scholar]
  48. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  49. Joudaki, S., Mead, A., Blake, C., et al. 2017, MNRAS, 471, 1259 [NASA ADS] [CrossRef] [Google Scholar]
  50. Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055 [Google Scholar]
  51. Kewley, L. J., Maier, C., Yabe, K., et al. 2013, ApJ, 774, L10 [Google Scholar]
  52. Khramtsov, V., Sergeyev, A., Spiniello, C., et al. 2019, A&A, 632, A56 [EDP Sciences] [Google Scholar]
  53. Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511 [Google Scholar]
  54. Kuijken, K. 2008, A&A, 482, 1053 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Kuijken, K. 2011, Messenger, 146, 8 [Google Scholar]
  56. Kuijken, K., Heymans, C., Hildebrandt, H., et al. 2015, MNRAS, 454, 3500 [Google Scholar]
  57. Kuijken, K., Heymans, C., Dvornik, A., et al. 2019, A&A, 625, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Kurcz, A., Bilicki, M., Solarz, A., et al. 2016, A&A, 592, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Laurent, P., Eftekharzadeh, S., Le Goff, J.-M., et al. 2017, JCAP, 7, 017 [CrossRef] [Google Scholar]
  60. Leistedt, B., Peiris, H. V., & Roth, N. 2014, Phys. Rev. Lett., 113 [Google Scholar]
  61. Lindegren, L., Hernández, J., Bombrun, A., et al. 2018, A&A, 616, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Logan, C. H. A., & Fotopoulou, S. 2020, A&A, 633, A154 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Lyke, B. W., Higley, A. N., McLane, J. N., et al. 2020, ApJS, 250, 8 [NASA ADS] [CrossRef] [Google Scholar]
  64. Maddox, N., Hewett, P. C., Warren, S. J., & Croom, S. M. 2008, MNRAS, 386, 1605 [NASA ADS] [CrossRef] [Google Scholar]
  65. Małek, K., Buat, V., Burgarella, D., et al. 2020, in IAU Symp., eds. M. Boquien, E. Lusso, C. Gruppioni, & P. Tissera, 341, 39 [Google Scholar]
  66. McInnes, L., Healy, J., & Astels, S. 2017, J. Open Source Software, 2 [Google Scholar]
  67. Merloni, A., Alexander, D. A., Banerji, M., et al. 2019, Messenger, 175, 42 [NASA ADS] [Google Scholar]
  68. Nakoneczny, S., Bilicki, M., Solarz, A., et al. 2019, A&A, 624, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  69. Palanque-Delabrouille, N., Magneville, C., Yèche, C., et al. 2016, A&A, 587, A41 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  70. Pasquet-Itam, J., & Pasquet, J. 2018, A&A, 611, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  71. Pearson, K. 1901, London Edinburgh Dublin Philos. Mag. J. Sci., 2, 559 [Google Scholar]
  72. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  73. Prokhorenkova, L., Gusev, G., Vorobev, A., et al. 2018, in Advances in Neural Information Processing Systems 31, eds. S. Bengio, H. Wallach, H. Larochelle, et al., 6638 [Google Scholar]
  74. Richards, G. T., Fan, X., Newberg, H. J., et al. 2002, AJ, 123, 2945 [NASA ADS] [CrossRef] [Google Scholar]
  75. Richards, G. T., Nichol, R. C., Gray, A. G., et al. 2004, ApJS, 155, 257 [NASA ADS] [CrossRef] [Google Scholar]
  76. Richards, G. T., Myers, A. D., Gray, A. G., et al. 2009a, ApJS, 180, 67 [NASA ADS] [CrossRef] [Google Scholar]
  77. Richards, G. T., Deo, R. P., Lacy, M., et al. 2009b, AJ, 137, 3884 [NASA ADS] [CrossRef] [Google Scholar]
  78. Richards, G. T., Myers, A. D., Peters, C. M., et al. 2015, ApJS, 219, 39 [NASA ADS] [CrossRef] [Google Scholar]
  79. Richard, J., Kneib, J. P., Blake, C., et al. 2019, Messenger, 175, 50 [Google Scholar]
  80. Salvato, M., Hasinger, G., Ilbert, O., et al. 2009, ApJ, 690, 1250 [CrossRef] [Google Scholar]
  81. Salvato, M., Ilbert, O., Hasinger, G., et al. 2011, ApJ, 742, 61 [NASA ADS] [CrossRef] [Google Scholar]
  82. Scranton, R., Ménard, B., Richards, G. T., et al. 2005, ApJ, 633, 589 [NASA ADS] [CrossRef] [Google Scholar]
  83. Secrest, N. J., Dudik, R. P., Dorland, B. N., et al. 2015, ApJS, 221, 12 [NASA ADS] [CrossRef] [Google Scholar]
  84. Sherwin, B. D., Das, S., Hajian, A., et al. 2012, Phys. Rev. D, 86 [CrossRef] [Google Scholar]
  85. Shu, Y., Koposov, S. E., Evans, N. W., et al. 2019, MNRAS, 489, 4741 [Google Scholar]
  86. Śmieja, M., Struski, L. U., Tabor, J., Zieliński, B., Spurek, P. A., et al. 2018, in Advances in Neural Information Processing Systems 31, eds. S. Bengio, H. Wallach, H. Larochelle, et al., 2719 [Google Scholar]
  87. Spiniello, C., Agnello, A., Napolitano, N. R., et al. 2018, MNRAS, 480, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  88. Stalevski, M., Ricci, C., Ueda, Y., et al. 2016, MNRAS, 458, 2288 [NASA ADS] [CrossRef] [Google Scholar]
  89. Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30 [NASA ADS] [CrossRef] [Google Scholar]
  90. Stölzner, B., Cuoco, A., Lesgourgues, J., & Bilicki, M. 2018, Phys. Rev. D, 97, 063514 [Google Scholar]
  91. van der Maaten, L., & Hinton, G. 2008, J. Mach. Learn. Res., 9, 2579 [Google Scholar]
  92. van Uitert, E., Joachimi, B., Joudaki, S., et al. 2018, MNRAS, 476, 4662 [Google Scholar]
  93. Venemans, B. P., Verdoes Kleijn, G. A., Mwebaze, J., et al. 2015, MNRAS, 453, 2259 [NASA ADS] [CrossRef] [Google Scholar]
  94. Warren, S. J., Hewett, P. C., & Foltz, C. B. 2000, MNRAS, 312, 827 [NASA ADS] [CrossRef] [Google Scholar]
  95. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [Google Scholar]
  96. Wright, A. H., Hildebrandt, H., van den Busch, J. L., et al. 2020, A&A, 640, L14 [CrossRef] [EDP Sciences] [Google Scholar]
  97. Wu, X.-B., Hao, G., Jia, Z., Zhang, Y., & Peng, N. 2012, AJ, 144, 49 [NASA ADS] [CrossRef] [Google Scholar]
  98. Yang, Q., Wu, X.-B., Fan, X., et al. 2017, AJ, 154, 269 [NASA ADS] [CrossRef] [Google Scholar]
  99. Yang, G., Boquien, M., Buat, V., et al. 2020, MNRAS, 491, 740 [NASA ADS] [CrossRef] [Google Scholar]
  100. York, D. G., Adelman, J., Anderson, J. E., Jr, et al. 2000, AJ, 120, 1579 [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.