EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 666 (October 2022) A&A, 666 (2022) A171 References
Open Access
Issue
A&A
Volume 666, October 2022
Article Number A171
Number of page(s) 24
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202243900
Published online 25 October 2022
  1. Agostino, C.J., & Salim, S. 2019, ApJ, 876, 12 [NASA ADS] [CrossRef] [Google Scholar]
  2. Ahumada, R., Prieto, C.A., Almeida, A., et al. 2020, ApJS, 249, 3 [NASA ADS] [CrossRef] [Google Scholar]
  3. Aihara, H., AlSayyad, Y., Ando, M., et al. 2019, PASJ, 71, 114 [Google Scholar]
  4. Álvarez-Márquez, J., Colina, L., Marques-Chaves, R., et al. 2019, A&A, 629, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Awang Iskandar, D.N.F., Zijlstra, A.A., McDonald, I., et al. 2020, Galaxies, 8, 88 [NASA ADS] [CrossRef] [Google Scholar]
  6. Baldwin, J.A., Phillips, M.M., & Terlevich, R. 1981, PASP, 93, 5 [NASA ADS] [CrossRef] [Google Scholar]
  7. Baron, D. 2019, ArXiv e-prints [arXiv:1904.07248] [Google Scholar]
  8. Baron, D., & Poznanski, D. 2017, MNRAS, 465, 4530 [NASA ADS] [CrossRef] [Google Scholar]
  9. Birchall, K.L., Watson, M.G., & Aird, J. 2020, MNRAS, 492, 2268 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bishop, C.M. 2006, Pattern Recognition and Machine Learning (Springer) [Google Scholar]
  11. Blanton, M.R., Bershady, M.A., Abolfathi, B., et al. 2017, AJ, 154, 28 [NASA ADS] [CrossRef] [Google Scholar]
  12. Brandt, W., & Hasinger, G. 2005, ArA&A, 43, 827 [NASA ADS] [CrossRef] [Google Scholar]
  13. Brescia, M., Cavuoti, S., D’Abrusco, R., Longo, G., & Mercurio, A. 2013, ApJ, 772, 140 [NASA ADS] [CrossRef] [Google Scholar]
  14. Brescia, M., Salvato, M., Cavuoti, S., et al. 2019, MNRAS, 489, 663 [NASA ADS] [CrossRef] [Google Scholar]
  15. Brinchmann, J., Charlot, S., White, S., et al. 2004, MNRAS, 351, 1151 [NASA ADS] [CrossRef] [Google Scholar]
  16. Byrne, L., Christensen, C., Tsekitsidis, M., Brooks, A., & Quinn, T. 2019, ApJ, 871, 213 [NASA ADS] [CrossRef] [Google Scholar]
  17. Castro-Ginard, A., Jordi, C., Luri, X., et al. 2018, A&A, 618, A59 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Cavuoti, S., Brescia, M., D’Abrusco, R., Longo, G., & Paolillo, M. 2013, MNRAS, 437, 968 [Google Scholar]
  19. Chang, Y.-Y., Hsieh, B.-C., Wang, W.-H., et al. 2021, ApJ, 920, 68 [NASA ADS] [CrossRef] [Google Scholar]
  20. Chen, Y.C. 2021, ApJS, 256, 34 [NASA ADS] [CrossRef] [Google Scholar]
  21. Chung, S.M., Kochanek, C.S., Assef, R., et al. 2014, ApJ, 790, 54 [NASA ADS] [CrossRef] [Google Scholar]
  22. Dark Energy Survey Collaboration, Abbott, T., Abdalla, F.B., et al. 2016, MNRAS, 460, 1270 [NASA ADS] [CrossRef] [Google Scholar]
  23. De Cicco, D., Bauer, F., Paolillo, M., et al. 2021, A&A, 645, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. 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]
  25. Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. 2019, MNRAS, 486, 1377 [Google Scholar]
  26. Deng, J., Dong, W., Socher, R., et al. 2009, in 2009 IEEE conference on computer vision and pattern recognition (IEEE), 248 [CrossRef] [Google Scholar]
  27. Ding, Y., Sohn, J.H., Kawczynski, M.G., et al. 2019, Radiology, 290, 456 [CrossRef] [Google Scholar]
  28. D’Isanto, A., & Polsterer, K.L. 2018, A&A, 609, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. D’Isanto, A., Cavuoti, S., Brescia, M., et al. 2016, MNRAS, 457, 3119 [CrossRef] [Google Scholar]
  30. D’Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K.L. 2018, A&A, 616, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Eisenstein, D.J., Weinberg, D.H., Agol, E., et al. 2011, AJ, 142, 72 [NASA ADS] [CrossRef] [Google Scholar]
  32. Esteva, A., Kuprel, B., Novoa, R.A., et al. 2017, Nature, 542, 115 [CrossRef] [Google Scholar]
  33. Euclid Collaboration (Desprez, G., et al.) 2020, A&A, 644, A31 [EDP Sciences] [Google Scholar]
  34. Fabian, A.C. 2012, ARA&A, 50, 455 [CrossRef] [Google Scholar]
  35. Faisst, A., Prakash, A., Capak, P., & Lee, B. 2019, ApJL, 881 [Google Scholar]
  36. Falocco, S., Carrera, F.J., & Larsson, J. 2022, MNRAS, 510, 161 [Google Scholar]
  37. Fluke, C.J., & Jacobs, C. 2020, WIREs Data Mining Knowl. Discov., 10, e1349 [CrossRef] [Google Scholar]
  38. Fotopoulou, S., & Paltani, S. 2018, A&A, 619, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Frontera-Pons, J., Sureau, F., Bobin, J., & Le Floch, E. 2017, A&A, 603, A60 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. Goebel, R., Chander, A., Holzinger, K., et al. 2018, in Machine Learning and Knowledge Extraction, eds. A. Holzinger, P. Kieseberg, A.M. Tjoa, & E. Weippl (Cham: Springer International Publishing), 295 [CrossRef] [Google Scholar]
  41. Goodfellow, I., Bengio, Y., & Courville, A. 2016, Deep Learning (MIT Press), http://www.deeplearningbook.org [Google Scholar]
  42. Goudfrooij, P. 1995, PASP, 107, 502 [NASA ADS] [CrossRef] [Google Scholar]
  43. Green, J., Schechter, P., Baltay, C., et al. 2012, ArXiv e-prints [arXiv:1208.4012] [Google Scholar]
  44. Häring, N., & Rix, H.-W. 2004, ApJ, 604, L89 [Google Scholar]
  45. Hastie, T., Tibshirani, R., Friedman, J.H., & Friedman, J.H. 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2 (Springer) [Google Scholar]
  46. Heckman, T.M., 1980, A&A, 87, 152 [NASA ADS] [Google Scholar]
  47. Heckman, T.M., & Best, P.N. 2014, ARA&A, 52, 589 [NASA ADS] [CrossRef] [Google Scholar]
  48. Heinis, S., Gezari, S., Kumar, S., et al. 2016, ApJ, 826, 62 [NASA ADS] [CrossRef] [Google Scholar]
  49. Hickox, R.C., & Alexander, D.M. 2018, ARA&A, 56, 625 [NASA ADS] [CrossRef] [Google Scholar]
  50. Hirashita, H., Nozawa, T., Villaume, A., & Srinivasan, S. 2015, MNRAS, 454, 1620 [NASA ADS] [CrossRef] [Google Scholar]
  51. Ivezic, Z., Kahn, S.M., Tyson, J.A., et al. 2019, ApJ, 873, 111 [NASA ADS] [CrossRef] [Google Scholar]
  52. Ji, Z., Giavalisco, M., Kirkpatrick, A., et al. 2022, ApJ, 925, 74 [NASA ADS] [CrossRef] [Google Scholar]
  53. Kauffmann, G., Heckman, T.M., Tremonti, C., et al. 2003, MNRAS, 346, 1055 [NASA ADS] [CrossRef] [Google Scholar]
  54. Kewley, L.J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961 [NASA ADS] [CrossRef] [Google Scholar]
  55. Kim, D.-W., Protopapas, P., Byun, Y.-I., et al. 2011, ApJ, 735, 68 [NASA ADS] [CrossRef] [Google Scholar]
  56. Kim, Y.J., Bae, J.P., Chung, J.-W., et al. 2021, Sci. Rep., 11, 3605 [NASA ADS] [CrossRef] [Google Scholar]
  57. Kinson, D.A., Oliveira, J.M., & van Loon, J.T. 2021, MNRAS, 507, 5106 [NASA ADS] [CrossRef] [Google Scholar]
  58. Kormendy, J., & Ho, L.C. 2013, ARA&A, 51, 511 [NASA ADS] [CrossRef] [Google Scholar]
  59. Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. 1998, Proc. IEEE, 86, 2278 [Google Scholar]
  60. Lianou, S., Barmby, P., Mosenkov, A.A., Lehnert, M., & Karczewski, O. 2019, A&A, 631, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Longo, G., Merényi, E., & Tino, P. 2019, PASP, 131, 100101 [NASA ADS] [CrossRef] [Google Scholar]
  62. Lutz, D., Mainieri, V., Rafferty, D., et al. 2010, ApJ, 712, 1287 [NASA ADS] [CrossRef] [Google Scholar]
  63. Magnier, E.A., Schlafly, E.F., Finkbeiner, D.P., et al. 2020, ApJS, 251, 6 [NASA ADS] [CrossRef] [Google Scholar]
  64. Martinazzo, A., Espadoto, M., & Hirata, N.S. 2021, in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 4176 [CrossRef] [Google Scholar]
  65. Masters, D., Capak, P., Stern, D., et al. 2015, ApJ, 813, 53 [Google Scholar]
  66. Mateos, S., Alonso-Herrero, A., Carrera, F.J., et al. 2012, MNRAS, 426, 3271 [NASA ADS] [CrossRef] [Google Scholar]
  67. McConnell, N.J., & Ma, C.-P. 2013, ApJ, 764, 184 [NASA ADS] [CrossRef] [Google Scholar]
  68. McCulloch, W.S., & Pitts, W. 1943, Bull. Math. Biophys., 5, 115 [CrossRef] [Google Scholar]
  69. Mendez, A.J., Coil, A.L., Aird, J., et al. 2013, ApJ, 770, 40 [NASA ADS] [CrossRef] [Google Scholar]
  70. Menegola, A., Fornaciali, M., Pires, R., et al. 2017, in 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), IEEE, 297 [CrossRef] [Google Scholar]
  71. Merloni, A. 2016, in Astrophysical Black Holes, eds. F. Haardt, V. Gorini, U. Moschella, A. Treves, & M. Colpi (Cham: Springer International Publishing), 101 [NASA ADS] [CrossRef] [Google Scholar]
  72. Mislis, D., Pyrzas, S., & Alsubai, K.A. 2018, MNRAS, 481, 1624 [NASA ADS] [CrossRef] [Google Scholar]
  73. Mullaney, J.R., Pannella, M., Daddi, E., et al. 2012, MNRAS, 419, 95 [NASA ADS] [CrossRef] [Google Scholar]
  74. Ofman, L., Averbuch, A., Shliselberg, A., et al. 2022, New A, 91, 101693 [NASA ADS] [CrossRef] [Google Scholar]
  75. Palaversa, L., Ivezic, Z., Eyer, L., et al. 2013, AJ, 146, 101 [CrossRef] [Google Scholar]
  76. Pan, S.J., & Yang, Q. 2009, IEEE Trans. Knowl. Data Eng., 22, 1345 [Google Scholar]
  77. Prima, B., & Bouhorma, M. 2020, ISPRS, 4443, 343 [NASA ADS] [Google Scholar]
  78. Razim, O., Cavuoti, S., Brescia, M., et al. 2021, MNRAS, 507, 5034 [NASA ADS] [CrossRef] [Google Scholar]
  79. Richards, G.T., Fan, X., Newberg, H.J., et al. 2002, AJ, 123, 2945 [NASA ADS] [CrossRef] [Google Scholar]
  80. Richards, G.T., Croom, S.M., Anderson, S.F., et al. 2005, MNRAS, 360, 839 [NASA ADS] [CrossRef] [Google Scholar]
  81. Rosario, D.J., Santini, P., Lutz, D., et al. 2013, ApJ, 771, 63 [NASA ADS] [CrossRef] [Google Scholar]
  82. Rosen, S.R., Webb, N.A., Watson, M.G., et al. 2016, A&A, 590, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  83. Rosenblatt, F. 1958, Psychol. Rev., 65 [Google Scholar]
  84. Scaramella, R., Amiaux, J., Mellier, Y., et al. 2022, A&A, 662, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Schmidhuber, J. 2015, Neural Netw., 61, 85 [CrossRef] [Google Scholar]
  87. Schmidt, S.J., Malz, A.I., Soo, J.Y.H., et al. 2020, MNRAS, 499, 1587 [NASA ADS] [Google Scholar]
  88. Schneider, D.P., Hall, P.B., Richards, G.T., et al. 2007, AJ, 134, 102 [NASA ADS] [CrossRef] [Google Scholar]
  89. Schneider, D.P., Richards, G.T., Hall, P.B., et al. 2010, AJ, 139, 2360 [NASA ADS] [CrossRef] [Google Scholar]
  90. Shakura, N.I., & Sunyaev, R.A. 1973, A&A, 24, 337 [Google Scholar]
  91. Shimizu, T.T., Mushotzky, R.F., Meléndez, M., Koss, M., & Rosario, D.J. 2015, MNRAS, 452, 1841 [NASA ADS] [CrossRef] [Google Scholar]
  92. Stein, G., Blaum, J., Harrington, P., Medan, T., & Lukic, Z. 2022, ApJ, 932, 107 [NASA ADS] [CrossRef] [Google Scholar]
  93. Stemo, A., Comerford, J.M., Barrows, R.S., et al. 2020, ApJ, 888, 78 [NASA ADS] [CrossRef] [Google Scholar]
  94. Sánchez-Sáez, P., Lira, P., Cartier, R., et al. 2019, ApJS, 242 [Google Scholar]
  95. Tan, M., & Le, Q. 2019, in International Conference on Machine Learning, PMLR, 6105 [Google Scholar]
  96. Thacker, R.J., MacMackin, C., Wurster, J., & Hobbs, A. 2014, MNRAS, 443, 1125 [NASA ADS] [CrossRef] [Google Scholar]
  97. Thom, C., Tumlinson, J., Werk, J.K., et al. 2012, ApJ, 758, L41 [NASA ADS] [CrossRef] [Google Scholar]
  98. Thomas, D., Maraston, C., & Bender, R. 2002, Ap&SS, 281, 371 [NASA ADS] [CrossRef] [Google Scholar]
  99. Torbaniuk, O., Paolillo, M., Carrera, F., et al. 2021, MNRAS, 506, 2619 [NASA ADS] [CrossRef] [Google Scholar]
  100. Trump, J.R., Konidaris, N.P., Barro, G., et al. 2013, ApJ, 763, L6 [NASA ADS] [CrossRef] [Google Scholar]
  101. Wang, Y., Yao, Q., Kwok, J.T., & Ni, L.M. 2020, ACM Comput. Surv. (CSUR), 53, 1 [Google Scholar]
  102. Weir, N., Fayyad, U.M., & Djorgovski, S. 1995, AJ, 109, 2401 [NASA ADS] [CrossRef] [Google Scholar]
  103. Wenzl, L., Schindler, J.-T., Fan, X., et al. 2021, AJ, 162, 72 [NASA ADS] [CrossRef] [Google Scholar]
  104. York, D.G., Adelman, J., Anderson, J.E., Jr., et al. 2000, AJ, 120, 1579 [NASA ADS] [CrossRef] [Google Scholar]
  105. Yu, W., & Richards, G. 2021, LSSTC AGN Data Challenge, https://github.com/RichardsGroup/AGN_DataChallenge [CrossRef] [Google Scholar]
  106. Zhang, K., & Hao, L. 2018, ApJ, 856, 171 [NASA ADS] [CrossRef] [Google Scholar]
  107. Zou, M., & Zhong, Y. 2018, Sens. Imaging, 19, 6 [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.