Open Access
Issue
A&A
Volume 690, October 2024
Article Number A211
Number of page(s) 42
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202449548
Published online 09 October 2024
  1. Akeret, J., Chang, C., Lucchi, A., & Refregier, A. 2017, Astron. Comput., 18, 35 [NASA ADS] [CrossRef] [Google Scholar]
  2. Banfield, J. K., Wong, O. I., Willett, K. W., et al. 2015, MNRAS, 453, 2326 [Google Scholar]
  3. Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Bianco, M., Giri, S. K., Iliev, I. T., & Mellema, G. 2021, MNRAS, 505, 3982 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bonaldi, A., Bonato, M., Galluzzi, V., et al. 2019, MNRAS, 482, 2 [Google Scholar]
  6. Bonaldi, A., An, T., Brüggen, M., et al. 2021, MNRAS, 500, 3821 [Google Scholar]
  7. Braun, R., Bourke, T., Green, J., Keane, E., & Wagg, J. 2015, in Conférence: Advancing Astrophysics with the Square Kilometre Array, 174 [CrossRef] [Google Scholar]
  8. Burke, C. J., Aleo, P. D., Chen, Y.-C., et al. 2019, MNRAS, 490, 3952 [NASA ADS] [CrossRef] [Google Scholar]
  9. Carbone, D., Garsden, H., Spreeuw, H., et al. 2018, Astron. Comput., 23, 92 [NASA ADS] [CrossRef] [Google Scholar]
  10. Carion, N., Massa, F., Synnaeve, G., et al. 2020, arXiv e-prints [arXiv:2005.12872] [Google Scholar]
  11. Clarke, A., & Collinson, J. 2021, https://doi.org/10.5281/zenodo.5526844 [Google Scholar]
  12. Cornu, D. 2024a, https://doi.org/10.5281/zenodo.12801421 [Google Scholar]
  13. Cornu, D. 2024b, https://doi.org/10.5281/zenodo.12806325 [Google Scholar]
  14. Cornu, D. 2024c, https://doi.org/10.5281/zenodo.13141772 [Google Scholar]
  15. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. 2010, Int. J. Comput. Vision, 88, 303 [CrossRef] [Google Scholar]
  16. Fang, Y., Liao, B., Wang, X., et al. 2021, in Advances in Neural Information Processing Systems, eds. M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. W. Vaughan (New York: Curran Associates, Inc.), 34, 26183 [Google Scholar]
  17. Farias, H., Ortiz, D., Damke, G., Jaque Arancibia, M., & Solar, M. 2020, Astron. Comput., 33, 100420 [NASA ADS] [CrossRef] [Google Scholar]
  18. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. 2010, IEEE Trans. Pattern Anal. Mach. Intell., 32, 1627 [CrossRef] [Google Scholar]
  19. Gal, Y., & Ghahramani, Z. 2016, Proc. Mach. Learn. Res., 48, 1050 [Google Scholar]
  20. Girshick, R., Donahue, J., Darrell, T., & Malik, J. 2013, arXiv e-prints [arXiv:1311.2524] [Google Scholar]
  21. Glorot, X., & Bengio, Y. 2010, Proc. Mach. Learn. Res., 9, 249 [Google Scholar]
  22. González, R. E., Muñoz, R. P., & Hernández, C. A. 2018, Astron. Comput., 25, 103 [CrossRef] [Google Scholar]
  23. Grishin, K., Mei, S., & Ilic, S. 2023, A&A, 677, A101 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Gupta, N., Hayder, Z., Norris, R. P., Huynh, M., & Petersson, L. 2024, PASA, 41, e001 [NASA ADS] [CrossRef] [Google Scholar]
  25. Håkansson, H., Sjöberg, A., Toribio, M. C., et al. 2023, A&A, 671, A39 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  26. Hales, C. A., Murphy, T., Curran, J. R., et al. 2012, MNRAS, 425, 979 [Google Scholar]
  27. Hancock, P. J., Trott, C. M., & Hurley-Walker, N. 2018, PASA, 35, e011 [Google Scholar]
  28. Hartley, P., Bonaldi, A., Braun, R., et al. 2023, MNRAS, 523, 1967 [NASA ADS] [CrossRef] [Google Scholar]
  29. He, K., Zhang, X., Ren, S., & Sun, J. 2016, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
  30. He, K., Gkioxari, G., Dollar, P., & Girshick, R. 2017, arXiv e-prints [arXiv:1703.06870] [Google Scholar]
  31. He, Z., Qiu, B., Luo, A. L., et al. 2021, MNRAS, 508, 2039 [NASA ADS] [CrossRef] [Google Scholar]
  32. He, Y., Wu, J., Wang, W., Jiang, B., & Zhang, Y. 2023, PASJ, 75, 1311 [NASA ADS] [CrossRef] [Google Scholar]
  33. Hopkins, A. M., Miller, C. J., Connolly, A. J., et al. 2002, AJ, 123, 1086 [NASA ADS] [CrossRef] [Google Scholar]
  34. Huertas-Company, M., & Lanusse, F. 2023, PASA, 40, e001 [NASA ADS] [CrossRef] [Google Scholar]
  35. Ioffe, S., & Szegedy, C. 2015, Proc. Mach. Learn. Res., 37, 448 [Google Scholar]
  36. Jia, P., Liu, Q., & Sun, Y. 2020, AJ, 159, 212 [NASA ADS] [CrossRef] [Google Scholar]
  37. Knödlseder, J., Brau-Nogué, S., Coriat, M., et al. 2022, Nat. Astron., 6, 503 [CrossRef] [Google Scholar]
  38. Kuhn, H. W. 1955, Naval Res. Logistics Quarter., 2, 83 [CrossRef] [Google Scholar]
  39. Lao, B., An, T., Wang, A., et al. 2021, Sci. Bull., 66, 2145 [NASA ADS] [CrossRef] [Google Scholar]
  40. LeCun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 [Google Scholar]
  41. Lin, T.-Y., Maire, M., Belongie, S., et al. 2014, arXiv e-prints [arXiv: 1405.0312] [Google Scholar]
  42. Lin, T.-Y., Dollar, P., Girshick, R., et al. 2017, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
  43. Lintott, C. J., Schawinski, K., Slosar, A., et al. 2008, MNRAS, 389, 1179 [NASA ADS] [CrossRef] [Google Scholar]
  44. Liu, W., Anguelov, D., Erhan, D., et al. 2015, arXiv e-prints [arXiv: 1512.02325] [Google Scholar]
  45. Lucas, L., Staley, T., & Scaife, A. 2019, Astron. Comput., 27, 96 [NASA ADS] [CrossRef] [Google Scholar]
  46. Lukic, V., de Gasperin, F., & Brüggen, M. 2019, Galaxies, 8, 3 [NASA ADS] [CrossRef] [Google Scholar]
  47. Makinen, T. L., Lancaster, L., Villaescusa-Navarro, F., et al. 2021, J. Cosmology Astropart. Phys., 2021, 081 [CrossRef] [Google Scholar]
  48. McConnell, D., Hale, C. L., Lenc, E., et al. 2020, PASA, 37, e048 [CrossRef] [Google Scholar]
  49. Mohan, N., & Rafferty, D. 2015, Astrophysics Source Code Library [record ascl:1502.007] [Google Scholar]
  50. Molinari, S., Schisano, E., Faustini, F., et al. 2011, A&A, 530, A133 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Munkres, J. 1957, J. Soc. Industrial Appl. Math., 5, 32 [CrossRef] [Google Scholar]
  52. Ndung‘u, S., Grobler, T., Wijnholds, S. J., Karastoyanova, D., & Azzopardi, G. 2023, New A Rev., 97, 101685 [CrossRef] [Google Scholar]
  53. Paillassa, M., Bertin, E., & Bouy, H. 2020, A&A, 634, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. 2016, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
  55. Redmon, J., & Farhadi, A. 2017, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
  56. Redmon, J., & Farhadi, A. 2018, arXiv e-prints [arXiv:1804.02767] [Google Scholar]
  57. Ren, S., He, K., Girshick, R., & Sun, J. 2015, arXiv e-prints [arXiv:1506.01497] [Google Scholar]
  58. Rezatofighi, H., Tsoi, N., Gwak, J., et al. 2019, in Proceedings of the IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR) [Google Scholar]
  59. Riggi, S., Vitello, F., Becciani, U., et al. 2019, PASA, 36, e037 [NASA ADS] [CrossRef] [Google Scholar]
  60. Riggi, S., Magro, D., Sortino, R., et al. 2023, Astron. Comput., 42, 100682 [NASA ADS] [CrossRef] [Google Scholar]
  61. Robotham, A. S. G., Davies, L. J. M., Driver, S. P., et al. 2018, MNRAS, 476, 3137 [NASA ADS] [CrossRef] [Google Scholar]
  62. Ronneberger, O., Fischer, P., & Brox, T. 2015, arXiv e-prints [arXiv:1505.04597] [Google Scholar]
  63. Russakovsky, O., Deng, J., Su, H., et al. 2015, Int. J. Comp. Vision, 115, 211 [CrossRef] [Google Scholar]
  64. Salome, P., Caillat, M., Moreau, N., & Ba, Y. A. 2021, https://doi.org/10.5281/zenodo.3696974 [Google Scholar]
  65. Scaife, A. M. M. 2020, Phil. Trans. R. Soc. A Math. Phys. Eng. Sci., 378, 20190060 [CrossRef] [Google Scholar]
  66. Shimwell, T. W., Hardcastle, M. J., Tasse, C., et al. 2022, A&A, 659, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Simonyan, K., & Zisserman, A. 2015, in 3rd International Conference on Learning Representations (ICLR 2015) (Computational and Biological Learning Society), 1 [Google Scholar]
  68. Sortino, R., Magro, D., Fiameni, G., et al. 2023, Exp. Astron., 56, 293 [NASA ADS] [CrossRef] [Google Scholar]
  69. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. 2014, J. Mach. Learn. Res., 15, 1929 [Google Scholar]
  70. Tolley, E., Korber, D., Galan, A., et al. 2022, Astron. Comput., 41, 100631 [NASA ADS] [CrossRef] [Google Scholar]
  71. Vafaei Sadr, A., Vos, E. E., Bassett, B. A., et al. 2019, MNRAS, 484, 2793 [CrossRef] [Google Scholar]
  72. Vaswani, A., Shazeer, N., Parmar, N., et al. 2017, arXiv e-prints [arXiv:1706.03762] [Google Scholar]
  73. Wang, X., Wei, J., Liu, Y., et al. 2021, Universe, 7, 211 [NASA ADS] [CrossRef] [Google Scholar]
  74. Whiting, M. T. 2012, MNRAS, 421, 3242 [NASA ADS] [CrossRef] [Google Scholar]
  75. Whiting, M., & Humphreys, B. 2012, PASA, 29, 371 [NASA ADS] [CrossRef] [Google Scholar]
  76. Wu, Y., & He, K. 2018, in Proceedings of the European Conference on Computer Vision (ECCV) [Google Scholar]
  77. Wu, C., Wong, O. I., Rudnick, L., et al. 2019, MNRAS, 482, 1211 [NASA ADS] [CrossRef] [Google Scholar]
  78. Xing, Y., Yi, Z., Liang, Z., et al. 2023, ApJS, 269, 59 [NASA ADS] [CrossRef] [Google Scholar]
  79. Yu, L., Liu, B., Zhu, Y., et al. 2022, MNRAS, 511, 4305 [NASA ADS] [CrossRef] [Google Scholar]
  80. Zhang, Z., Lu, X., Cao, G., et al. 2021, in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2799 [CrossRef] [Google Scholar]
  81. Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. 2019, IEEE Trans. on Neural Netw. Learn. Syst., 30, 3212 [CrossRef] [Google Scholar]
  82. Zheng, Z., Wang, P., Liu, W., et al. 2020, Proc. AAAI Conf. Artif. Intell., 34, 12993 [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.