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Home All issues Volume 711 (July 2026) A&A, 711 (2026) A13 References
Issue
A&A
Volume 711, July 2026
Euclid Quick Data Release (Q1)
Article Number A13
Number of page(s) 28
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202554611
Published online 30 June 2026
  1. Aggarwal, C. C., Hinneburg, A., & Keim, D. A. 2001, in Database Theory – ICDT 2001, eds. J. Van den Bussche, & V. Vianu (Berlin, Heidelberg: Springer), 420 [Google Scholar]
  2. Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. 2000, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD ’00 (New York, NY, USA: Association for Computing Machinery), 93 [Google Scholar]
  3. Brown, T. B., Mann, B., Ryder, N., et al. 2020, ArXiv e-prints [arXiv:2005.14165] [Google Scholar]
  4. Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7 [Google Scholar]
  5. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. 2020, ArXiv e-prints [arXiv:2002.05709] [Google Scholar]
  6. Cheng, T.-Y., Conselice, C. J., Aragón-Salamanca, A., et al. 2020, MNRAS, 493, 4209 [Google Scholar]
  7. Cheng, T.-Y., Huertas-Company, M., Conselice, C. J., et al. 2021, MNRAS, 503, 4446 [NASA ADS] [CrossRef] [Google Scholar]
  8. Chu, J., Tang, H., Xu, D., Lu, S., & Long, R. 2024, MNRAS, 528, 6354 [NASA ADS] [CrossRef] [Google Scholar]
  9. Cortes, C., Jackel, L. D., Solla, S. A., Vapnik, V., & Denker, J. S. 1993, Guide Proceedings (San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.), 327 [Google Scholar]
  10. Dark Energy Survey Collaboration (Abbott, T., et al.) 2016, MNRAS, 460, 1270 [Google Scholar]
  11. DESI Collaboration (Abareshi, B., et al.) 2022, AJ, 164, 207 [NASA ADS] [CrossRef] [Google Scholar]
  12. DESI Collaboration (Adame, A. G., et al.) 2024a, AJ, 167, 62 [NASA ADS] [CrossRef] [Google Scholar]
  13. DESI Collaboration (Adame, A. G., et al.) 2024b, AJ, 168, 58 [NASA ADS] [CrossRef] [Google Scholar]
  14. Diaz, F., & Madaio, M. 2023, ArXiv e-prints [arXiv:2307.03201] [Google Scholar]
  15. Dolatshah, M., Hadian, A., & Minaei-Bidgoli, B. 2015, ArXiv e-prints [arXiv:1511.00628] [Google Scholar]
  16. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 [Google Scholar]
  17. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., et al. 2019, MNRAS, 484, 93 [Google Scholar]
  18. El-Nouby, A., Klein, M., Zhai, S., et al. 2024, ArXiv e-prints [arXiv:2401.08541] [Google Scholar]
  19. Euclid Collaboration (Desprez, G., et al.) 2020, A&A, 644, A31 [EDP Sciences] [Google Scholar]
  20. Euclid Collaboration (Schirmer, M., et al.) 2022, A&A, 662, A92 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Euclid Collaboration (Bisigello, L., et al.) 2023, MNRAS, 520, 3529 [NASA ADS] [CrossRef] [Google Scholar]
  22. Euclid Collaboration (Humphrey, A., et al.) 2023, A&A, 671, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Euclid Collaboration (Aussel, B., et al.) 2024, A&A, 689, A274 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Euclid Collaboration (Enia, A., et al.) 2024, A&A, 691, A175 [NASA ADS] [Google Scholar]
  25. Euclid Collaboration (Leuzzi, L., et al.) 2024, A&A, 681, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Euclid Collaboration (Cropper, M., et al.) 2025, A&A, 697, A2 [Google Scholar]
  27. Euclid Collaboration (Jahnke, K., et al.) 2025, A&A, 697, A3 [Google Scholar]
  28. Euclid Collaboration (Mellier, Y., et al.) 2025, A&A, 697, A1 [Google Scholar]
  29. Euclid Collaboration (Aussel, H., et al.) 2026, A&A, 711, A1 (Euclid Q1 SI) [Google Scholar]
  30. Euclid Collaboration (Enia, A., et al.) 2026, A&A, 711, A14 (Euclid Q1 SI) [Google Scholar]
  31. Euclid Collaboration (McCracken, H. J., et al.) 2026, A&A, 711, A2 (Euclid Q1 SI) [Google Scholar]
  32. Euclid Collaboration (Polenta, G., et al.) 2026, A&A, 711, A3 (Euclid Q1 SI) [Google Scholar]
  33. Euclid Collaboration (Romelli, E., et al.) 2026, A&A, 711, A4 (Euclid Q1 SI) [Google Scholar]
  34. Euclid Collaboration (Tucci, M., et al.) 2026, A&A, 711, A5 (Euclid Q1 SI) [Google Scholar]
  35. Euclid Collaboration (Walmsley, M., et al.) 2026a, A&A, 711, A26 (Euclid Q1 SI) [Google Scholar]
  36. Euclid Collaboration (Walmsley, M., et al.) 2026b, A&A, 711, A9 (Euclid Q1 SI) [Google Scholar]
  37. Euclid Quick Release Q1. 2025, https://doi.org/10.57780/esa-2853f3b [Google Scholar]
  38. Fotopoulou, S. 2024, Astron. Comput., 48, 100851 [Google Scholar]
  39. Fukushima, K. 1980, Biol. Cybern., 36, 193 [CrossRef] [PubMed] [Google Scholar]
  40. Gai, M., Bove, M., Bonetta, G., Zago, D., & Cancelliere, R. 2024, MNRAS, 532, 1391 [Google Scholar]
  41. Grattafiori, A., Dubey, A., Jauhri, A., et al. 2024, ArXiv e-prints [arXiv:2407.21783] [Google Scholar]
  42. Gunn, J. E., Carr, M., Rockosi, C., et al. 1998, AJ, 116, 3040 [NASA ADS] [CrossRef] [Google Scholar]
  43. Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332 [NASA ADS] [CrossRef] [Google Scholar]
  44. Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. 2021, ApJ, 911, L33 [NASA ADS] [CrossRef] [Google Scholar]
  45. Henighan, T., Kaplan, J., Katz, M., et al. 2020, ArXiv e-prints [arXiv:2010.14701.] [Google Scholar]
  46. Hocking, A., Geach, J. E., Sun, Y., & Davey, N. 2018, MNRAS, 473, 1108 [CrossRef] [Google Scholar]
  47. Hoffmann, J., Borgeaud, S., Mensch, A., et al. 2022, ArXiv e-prints [arXiv:2203.15556] [Google Scholar]
  48. Huber, P. J. 1964, Ann. Math. Stat., 35, 73 [CrossRef] [Google Scholar]
  49. Huertas-Company, M., & Lanusse, F. 2023, PASA, 40, e001 [NASA ADS] [CrossRef] [Google Scholar]
  50. Huertas-Company, M., Iyer, K. G., Angeloudi, E., et al. 2024, A&A, 685, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111 [Google Scholar]
  52. Kaplan, J., McCandlish, S., Henighan, T., et al. 2020, ArXiv e-prints [arXiv:2001.08361] [Google Scholar]
  53. Kümmel, M., Álvarez-Ayllón, A., Bertin, E., et al. 2022, ArXiv e-prints [arXiv:2212.02428] [Google Scholar]
  54. Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv e-prints [arXiv:1110.3193] [Google Scholar]
  55. Liu, F. T., Ting, K. M., & Zhou, Z.-H. 2008, 2008 Eighth IEEE International Conference on Data Mining, 413 [Google Scholar]
  56. Loshchilov, I., & Hutter, F. 2017, ArXiv e-prints [arXiv:1711.05101] [Google Scholar]
  57. Mampaso, A., García de la Rosa, I., Arias, P., et al. 2022, Study on Environmental Sustainability at the Instituto de Astrofísica de Canarias and Proposed Actions, Technical Report, Instituto de Astrofísica de Canarias [Google Scholar]
  58. Martínez-Solaeche, G., García-Benito, R., González Delgado, R. M., et al. 2024, A&A, 688, A160 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. McInnes, L., Healy, J., & Melville, J. 2018, ArXiv e-prints [arXiv:1802.03426] [Google Scholar]
  60. Merlin, E., Fontana, A., Ferguson, H. C., et al. 2015, A&A, 582, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Merlin, E., Amorín, R., Castellano, M., et al. 2016, A&A, 590, A30 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Merlin, E., Pilo, S., Fontana, A., et al. 2019, A&A, 622, A169 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Merz, G., Liu, X., Schmidt, S., et al. 2025, Open J. Astrophys., 8, 40 [Google Scholar]
  64. Mishra-Sharma, S., Song, Y., & Thaler, J. 2024, ArXiv e-prints [arXiv:2403.08851] [Google Scholar]
  65. Mohale, K., & Lochner, M. 2024, MNRAS, 530, 1274 [NASA ADS] [CrossRef] [Google Scholar]
  66. Muennighoff, N., Rush, A. M., Barak, B., et al. 2023, ArXiv e-prints [arXiv:2305.16264] [Google Scholar]
  67. Nair, V., & Hinton, G. E. 2010, Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10 (Madison, WI, USA: Omnipress), 807 [Google Scholar]
  68. Pan, J.-S., Ting, Y.-S., Huang, Y., Yu, J., & Liu, J.-F. 2024, ArXiv e-prints [arXiv:2405.17156] [Google Scholar]
  69. Parker, L., Lanusse, F., Golkar, S., et al. 2024, MNRAS, 531, 4990 [Google Scholar]
  70. Pattnaik, R., Kartaltepe, J. S., & Binu, C. 2025, ApJ, submitted [arXiv:2501.01070] [Google Scholar]
  71. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  72. Radford, A., Kim, J. W., Hallacy, C., et al. 2021, International Conference on Machine Learning (PMLR), 8748 [Google Scholar]
  73. Rizhko, M., & Bloom, J. S. 2025, AJ, 170, 28 [Google Scholar]
  74. Rosenblatt, F. 1958, Psychol. Rev., 65, 386 [CrossRef] [Google Scholar]
  75. Salvato, M., Ilbert, O., & Hoyle, B. 2019, Nature, 3, 212 [Google Scholar]
  76. Sarmiento, R., Huertas-Company, M., Knapen, J. H., et al. 2021, ApJ, 921, 177 [NASA ADS] [CrossRef] [Google Scholar]
  77. Schaeffer, R., Khona, M., Robertson, Z., et al. 2023, ArXiv e-prints [arXiv:2303.14151] [Google Scholar]
  78. Sengupta, A., Goel, Y., & Chakraborty, T. 2025, ArXiv e-prints [arXiv:2505.00985] [Google Scholar]
  79. Siudek, M., Pucha, R., Mezcua, M., et al. 2024, A&A, 691, A308 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Slijepcevic, I. V., Scaife, A. M. M., Walmsley, M., et al. 2024, RASTI, 3, 19 [Google Scholar]
  81. Smith, M. J., & Geach, J. E. 2023, R. Soc. Open Sci., 10, 221454 [NASA ADS] [CrossRef] [Google Scholar]
  82. Smith, M. J., Roberts, R. J., Angeloudi, E., & Huertas-Company, M. 2024, ArXiv e-prints [arXiv:2405.14930] [Google Scholar]
  83. Stein, G., Harrington, P., Blaum, J., Medan, T., & Lukic, Z. 2021, ArXiv e-prints [arXiv:2110.13151] [Google Scholar]
  84. Strubell, E., Ganesh, A., & Mccallum, A. 2019, ACL Anthology, 3645 [Google Scholar]
  85. Tanaka, M., Coupon, J., Hsieh, B.-C., et al. 2018, PASJ, 70, S9 [Google Scholar]
  86. The Multimodal Universe Collaboration (Audenaert, J., et al.) 2024, ArXiv e-prints [arXiv:2412.02527] [Google Scholar]
  87. Vega-Ferrero, J., Domínguez Sánchez, H., Bernardi, M., et al. 2021, MNRAS, 506, 1927 [NASA ADS] [CrossRef] [Google Scholar]
  88. Vega-Ferrero, J., Huertas-Company, M., Costantin, L., et al. 2024, ApJ, 961, 51 [NASA ADS] [CrossRef] [Google Scholar]
  89. Walmsley, M., Lintott, C., Géron, T., et al. 2022a, MNRAS, 509, 3966 [Google Scholar]
  90. Walmsley, M., Scaife, A. M. M., Lintott, C., et al. 2022b, MNRAS, 513, 1581 [NASA ADS] [CrossRef] [Google Scholar]
  91. Walmsley, M., Géron, T., Kruk, S., et al. 2023, MNRAS, 526, 4768 [Google Scholar]
  92. Walmsley, M., Bowles, M., Scaife, A. M. M., et al. 2024, ArXiv e-prints [arXiv:2404.02973] [Google Scholar]
  93. Xu, B., Wang, N., Chen, T., & Li, M. 2015, ArXiv e-prints [arXiv:1505.00853] [Google Scholar]
  94. You, K. 2025, ArXiv e-prints [arXiv:2504.16318] [Google Scholar]
  95. Zhang, G., Helfer, T., Gagliano, A. T., Mishra-Sharma, S., & Ashley Villar, V. 2024, Mach. Learn.: Sci. Technol., 5, 045069 [Google Scholar]

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