EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 691 (November 2024) A&A, 691 (2024) A144 References
Open Access
Issue
A&A
Volume 691, November 2024
Article Number A144
Number of page(s) 13
Section Galactic structure, stellar clusters and populations
DOI https://doi.org/10.1051/0004-6361/202451059
Published online 08 November 2024
  1. Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, software available from https://www.tensorflow.org [Google Scholar]
  2. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  3. Akras, S., Guzman-Ramirez, L., & Gonçalves, D. R. 2019a, MNRAS, 488, 3238 [NASA ADS] [CrossRef] [Google Scholar]
  4. Akras, S., Leal-Ferreira, M. L., Guzman-Ramirez, L., & Ramos-Larios, G. 2019b, MNRAS, 483, 5077 [NASA ADS] [CrossRef] [Google Scholar]
  5. Almeida-Fernandes, F. 2020, The S-PLUS Calibration Pipeline And schedule data releases, https://sites.usp.br/splus/wp-content/uploads/sites/846/2020/12/14_T_13_almeida-fernandes.pdf [Google Scholar]
  6. Almeida-Fernandes, F., SamPedro, L., Herpich, F. R., et al. 2022, MNRAS, 511, 4590 [NASA ADS] [CrossRef] [Google Scholar]
  7. Armandroff, T. E., & Da Costa, G. S. 1991, AJ, 101, 1329 [Google Scholar]
  8. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Astropy Collaboration (Price-Whelan, A. M., et al.) 2018, AJ, 156, 123 [Google Scholar]
  10. Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. 2021, AJ, 161, 147 [Google Scholar]
  11. Balas, V., Roy, S., Sharma, D., & Samui, P. 2019, Handbook of Deep Learning Applications, Smart Innovation, Systems and Technologies (Berlin: Springer International Publishing) [CrossRef] [Google Scholar]
  12. Baron, D. 2019, arXiv e-prints [arXiv:1904.07248] [Google Scholar]
  13. Bilicki, M., Hoekstra, H., Brown, M. J. I., et al. 2018, A&A, 616, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Boeche, C., Siebert, A., Piffl, T., et al. 2014, A&A, 568, A71 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Camarillo, T., Mathur, V., Mitchell, T., & Ratra, B. 2018, PASP, 130, 024101 [NASA ADS] [CrossRef] [Google Scholar]
  16. Casagrande, L., Wolf, C., Mackey, A. D., et al. 2019, MNRAS, 482, 2770 [NASA ADS] [Google Scholar]
  17. Cenarro, A. J., Moles, M., Cristóbal-Hornillos, D., et al. 2019, A&A, 622, A176 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints [arXiv:1612.05560] [Google Scholar]
  19. Chiti, A., Frebel, A., Mardini, M. K., et al. 2021, ApJS, 254, 31 [NASA ADS] [CrossRef] [Google Scholar]
  20. Cole, A. A., Smecker-Hane, T. A., Tolstoy, E., Bosler, T. L., & Gallagher, J. S. 2004, MNRAS, 347, 367 [NASA ADS] [CrossRef] [Google Scholar]
  21. Dark Energy Survey Collaboration (Abbott, T., et al.) 2016, MNRAS, 460, 1270 [Google Scholar]
  22. Das, P., & Sanders, J. L. 2019, MNRAS, 484, 294 [NASA ADS] [CrossRef] [Google Scholar]
  23. Dias, B., & Parisi, M. C. 2020, A&A, 642, A197 [EDP Sciences] [Google Scholar]
  24. Dias, B., Barbuy, B., Saviane, I., et al. 2015, A&A, 573, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Dias, B., Barbuy, B., Saviane, I., et al. 2017, ASI Conf. Ser., 14, 17 [NASA ADS] [Google Scholar]
  26. Dotter, A. 2016, ApJS, 222, 8 [Google Scholar]
  27. Eggen, O. J., Lynden-Bell, D., & Sandage, A. R. 1962, ApJ, 136, 748 [NASA ADS] [CrossRef] [Google Scholar]
  28. Fluri, J., Kacprzak, T., Lucchi, A., et al. 2019, Phys. Rev. D, 100, 6 [CrossRef] [Google Scholar]
  29. Fukugita, M., Ichikawa, T., Gunn, J. E., et al. 1996, AJ, 111, 1748 [Google Scholar]
  30. Galarza, C. A., Daflon, S., Placco, V. M., et al. 2022, A&A, 657, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. García Pérez, A. E., Allende Prieto, C., Holtzman, J. A., et al. 2016, AJ, 151, 144 [Google Scholar]
  32. Geisler, D. 1986, PASP, 98, 762 [NASA ADS] [CrossRef] [Google Scholar]
  33. Green, G. 2018, J. Open Source Softw., 3, 695 [NASA ADS] [CrossRef] [Google Scholar]
  34. Gruel, N., Moles, M., Varela, J., et al. 2012, SPIE Conf. Ser., 8448, 84481V [NASA ADS] [Google Scholar]
  35. Grunblatt, S. K., Zinn, J. C., Price-Whelan, A. M., et al. 2021, ApJ, 916, 88 [NASA ADS] [CrossRef] [Google Scholar]
  36. Grus, J. 2019, Data Science from Scratch: First Principles with Python (USA: O’Reilly Media) [Google Scholar]
  37. Gu, J., Du, C., Jia, Y., et al. 2015, MNRAS, 452, 3092 [NASA ADS] [CrossRef] [Google Scholar]
  38. Géron, A. 2017, Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (USA: O’Reilly Media) [Google Scholar]
  39. Hayden, M. R., Holtzman, J. A., Bovy, J., et al. 2014, AJ, 147, 116 [NASA ADS] [CrossRef] [Google Scholar]
  40. Hettiarachchi, P., Hall, M. J., & Minns, A. W. 2005, J. Hydroinform., 7, 291 [CrossRef] [Google Scholar]
  41. Holtzman, J. A., Shetrone, M., Johnson, J. A., et al. 2015, AJ, 150, 148 [Google Scholar]
  42. Horta, D., Schiavon, R. P., Mackereth, J. T., et al. 2023, MNRAS, 520, 5671 [NASA ADS] [CrossRef] [Google Scholar]
  43. Huang, Y., Liu, X.-W., Zhang, H.-W., et al. 2015, Res. Astron. Astrophys., 15, 1240 [CrossRef] [Google Scholar]
  44. Huang, Y., Beers, T. C., Wolf, C., et al. 2022, ApJ, 925, 164 [NASA ADS] [CrossRef] [Google Scholar]
  45. IBM Cloud Education 2020, Deep Learning, https://www.ibm.com/cloud/learn/deep-learning [Google Scholar]
  46. Ivezić, Ž., Sesar, B., Jurić, M., et al. 2008, ApJ, 684, 287 [Google Scholar]
  47. James, D., Subramanian, S., Omkumar, A. O., et al. 2021, MNRAS, 508, 5854 [NASA ADS] [CrossRef] [Google Scholar]
  48. Jurić, M., Ivezić, Ž., Brooks, A., et al. 2008, ApJ, 673, 864 [Google Scholar]
  49. Kingma, D. P., & Ba, J. 2017, arXiv e-prints [arXiv:1412.6980] [Google Scholar]
  50. LeCun, Y., Bengio, Y., & Hinton, G. 2015, Nature, 521, 436 [Google Scholar]
  51. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. 2018, J. Mach. Learn. Res. 18, 1 [Google Scholar]
  52. Limberg, G., Souza, S. O., Pérez-Villegas, A., et al. 2022, ApJ, 935, 109 [NASA ADS] [CrossRef] [Google Scholar]
  53. Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K. 2016, ApJS, 225, 31 [NASA ADS] [CrossRef] [Google Scholar]
  54. López-Sanjuan, C., Yuan, H., Vázquez Ramió, H., et al. 2021, A&A, 654, A61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, in IEEE Symposium Series on Computational Intelligence (SSCI) (USA: IEEE) [Google Scholar]
  56. Majewski, S. R. 2004, PASA, 21, 197 [NASA ADS] [CrossRef] [Google Scholar]
  57. Majewski, S. R., Ostheimer, J. C., Kunkel, W. E., & Patterson, R. J. 2000, AJ, 120, 2550 [NASA ADS] [CrossRef] [Google Scholar]
  58. Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94 [NASA ADS] [CrossRef] [Google Scholar]
  59. Marín-Franch, A., Chueca, S., Moles, M., et al. 2012, SPIE Conf. Ser., 8450, 84503S [Google Scholar]
  60. McMahon, R. G., Banerji, M., Gonzalez, E., et al. 2013, The Messenger, 154, 35 [NASA ADS] [Google Scholar]
  61. Mendes de Oliveira, C., Ribeiro, T., Schoenell, W., et al. 2019, MNRAS, 489, 241 [NASA ADS] [CrossRef] [Google Scholar]
  62. Miller, A. 2015, A Photometric Machine-Learning Method to Infer Stellar Metallicity (Berlin: Springer) [Google Scholar]
  63. Mitchell, T. 1997, Machine Learning, McGraw-Hill International Editions (New York: McGraw-Hill) [Google Scholar]
  64. Myeong, G. C., Belokurov, V., Aguado, D. S., et al. 2022, ApJ, 938, 21 [NASA ADS] [CrossRef] [Google Scholar]
  65. Öhman, Y. 1934, ApJ, 80, 171 [CrossRef] [Google Scholar]
  66. O’Malley, T., Bursztein, E., Long, J., et al. 2019, KerasTuner, https://github.com/keras-team/keras-tuner [Google Scholar]
  67. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
  68. Perez, L., & Wang, J. 2017, arXiv e-prints [arXiv:1712.04621] [Google Scholar]
  69. Perottoni, H. D., Limberg, G., Amarante, J. A. S., et al. 2022, ApJ, 936, L2 [NASA ADS] [CrossRef] [Google Scholar]
  70. Reid, M. J. 1993, ARA&A, 31, 345 [NASA ADS] [CrossRef] [Google Scholar]
  71. Sammut, C., & Webb, G. I., eds. 2010, Adaptive System (Boston, MA: Springer US), 35 [Google Scholar]
  72. Samuel, A. L. 2000, IBM J. Res. Develop., 44, 206 [CrossRef] [Google Scholar]
  73. Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103 [Google Scholar]
  74. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [Google Scholar]
  75. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  76. Soumagnac, M. T., Abdalla, F. B., Lahav, O., et al. 2015, MNRAS, 450, 666 [NASA ADS] [CrossRef] [Google Scholar]
  77. S-PLUS. 2019, S-PLUS: Instrumentation, https://www.splus.iag.usp.br/instrumentation/ [Google Scholar]
  78. Thackeray, A. D. 1939, MNRAS, 99, 492 [CrossRef] [Google Scholar]
  79. Thomas, G. F., Annau, N., McConnachie, A., et al. 2019, ApJ, 886, 10 [NASA ADS] [CrossRef] [Google Scholar]
  80. Vivas, K. A., & Zinn, R. 2002, arXiv e-prints [arXiv:astro-ph/0212116] [Google Scholar]
  81. Walmsley, M., Smith, L., Lintott, C., et al. 2020, MNRAS, 491, 1554 [Google Scholar]
  82. Wang, B., Hu, S. J., Sun, L., & Freiheit, T. 2020, J. Manufactur. Syst., 56, 373 [CrossRef] [Google Scholar]
  83. Wang, C., Bai, Y., Yuan, H., et al. 2022, A&A, 664, A38 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  84. Warren, S. R., & Cole, A. A. 2009, MNRAS, 393, 272 [NASA ADS] [CrossRef] [Google Scholar]
  85. Wei, S., Chen, Z., Arumugasamy, S. K., & Chew, I. M. L. 2022, Environ. Sci. Ecotechnol., 11, 100172 [NASA ADS] [CrossRef] [Google Scholar]
  86. Whitten, D. D., Placco, V. M., Beers, T. C., et al. 2019, A&A, 622, A182 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  87. Whitten, D. D., Placco, V. M., Beers, T. C., et al. 2021, ApJ, 912, 147 [NASA ADS] [CrossRef] [Google Scholar]
  88. Wolf, C., Onken, C. A., Luvaul, L. C., et al. 2018, PASA, 35, e010 [Google Scholar]
  89. Yang, L., Yuan, H., Xiang, M., et al. 2022, A&A, 659, A181 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Yip, K. H., Nikolaou, N., Coronica, P., et al. 2019, AAS/Division for Extreme Solar Systems Abstracts, 51, 305.04 [NASA ADS] [Google Scholar]
  91. York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579 [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.