Open Access
Issue |
A&A
Volume 672, April 2023
|
|
---|---|---|
Article Number | A46 | |
Number of page(s) | 17 | |
Section | Galactic structure, stellar clusters and populations | |
DOI | https://doi.org/10.1051/0004-6361/202244766 | |
Published online | 27 March 2023 |
- Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org [Google Scholar]
- Adibekyan, V. Z., Santos, N. C., Sousa, S. G., & Israelian, G. 2011, A&A, 535, L11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Alzubaidi, L., Zhang, J., Humaidi, A. J., et al. 2021, J. Big Data, 8, 53 [CrossRef] [Google Scholar]
- Amarsi, A. M., Lind, K., Osorio, Y., et al. 2020, A&A, 642, A62 [EDP Sciences] [Google Scholar]
- Anders, F., Chiappini, C., Santiago, B. X., et al. 2018, A&A, 619, A125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bailer-Jones, C. A. L. 1997, The Observatory, 117, 250 [NASA ADS] [Google Scholar]
- Bergemann, M., Collet, R., Amarsi, A. M., et al. 2017, ApJ, 847, 15 [NASA ADS] [CrossRef] [Google Scholar]
- Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Bialopetravičius, J., & Narbutis, D. 2020, AJ, 160, 264 [CrossRef] [Google Scholar]
- Bishop, C. M. 1995, Neural Networks for Pattern Recognition (Oxford: Oxford University Press) [Google Scholar]
- Bovy, J. 2015, ApJS, 216, 29 [NASA ADS] [CrossRef] [Google Scholar]
- Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [Google Scholar]
- Campello, R. J. G. B., Moulavi, D., & Sander, J. 2013, in Advances in Knowledge Discovery and Data Mining, eds. J. Pei, V. S. Tseng, L. Cao, H. Motoda, & G. Xu (Berlin, Heidelberg: Springer-Verlag), 160 [Google Scholar]
- Chollet, F., et al. 2015, Keras, https://github.com/fchollet/keras [Google Scholar]
- Dalton, G., Trager, S., Abrams, D. C., et al. 2018, in Ground-based and Airborne Instrumentation for Astronomy VII, eds. C. J. Evans, L. Simard, & H. Takami, Int. Soc. Opt. Photon. (SPIE), 10702, 388 [Google Scholar]
- de Jong, R. S., Agertz, O., Berbel, A. A., et al. 2019, The Messenger, 175, 3 [NASA ADS] [Google Scholar]
- Fabbro, S., Venn, K. A., O’Briain, T., et al. 2018, MNRAS, 475, 2978 [Google Scholar]
- Fuhrmann, K. 1998, A&A, 338, 161 [NASA ADS] [Google Scholar]
- Gaia Collaboration (Prusti, T., et al.) 2016, A&A, 595, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Gaia Collaboration (Brown, A. G. A., et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Giancarlo, Z., & Md Rezaul, K. 2018, Deep Learning with TensorFlow: Explore Neural Networks and Build Intelligent Systems with Python, 2nd edn. (Packt Publishing) [Google Scholar]
- Gilmore, G., Randich, S., Asplund, M., et al. 2012, The Messenger, 147, 25 [NASA ADS] [Google Scholar]
- Gratton, R. G., Carretta, E., Matteucci, F., & Sneden, C. 2000, A&A, 358, 671 [NASA ADS] [Google Scholar]
- Grevesse, N., Asplund, M., & Sauval, A. J. 2007, Space Sci. Rev., 130, 105 [Google Scholar]
- Guiglion, G., Matijevič, G., Queiroz, A. B. A., et al. 2020, A&A, 644, A168 [EDP Sciences] [Google Scholar]
- Heiter, U., Jofré, P., Gustafsson, B., et al. 2015, A&A, 582, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Heiter, U., Lind, K., Bergemann, M., et al. 2021, A&A, 645, A106 [EDP Sciences] [Google Scholar]
- Jofré, P., Heiter, U., & Soubiran, C. 2019, ARA&A, 57, 571 [Google Scholar]
- Kendall, A., & Gal, Y. 2017, Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17 (Red Hook, NY, USA: Curran Associates Inc.), 5580 [Google Scholar]
- Kilic, M., Munn, J. A., Harris, H. C., et al. 2017, ApJ, 837, 162 [Google Scholar]
- Lahav, O., Naim, A., Sodré, L., Jr., & Storrie-Lombardi, M. C. 1996, MNRAS, 283, 207 [NASA ADS] [CrossRef] [Google Scholar]
- Leung, H. W., & Bovy, J. 2019, MNRAS, 483, 3255 [NASA ADS] [Google Scholar]
- Lind, K., Nordlander, T., Wehrhahn, A., et al. 2022, A&A, 665, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Maas, A. L., Hannun, A. Y., & Ng, A. Y. 2013, Proceedings of the International Conference on Machine Learning (ICML), 30, 3 [Google Scholar]
- Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94 [Google Scholar]
- Matijevič, G., Chiappini, C., Grebel, E. K., et al. 2017, A&A, 603, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Miglio, A., Chiappini, C., Mackereth, J. T., et al. 2021, A&A, 645, A85 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Mikolaitis, Š., Hill, V., Recio-Blanco, A., et al. 2014, A&A, 572, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Mints, A., & Hekker, S. 2017, A&A, 604, A108 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Nepal, S., Guiglion, G., de Jong, R. S., et al. 2023, A&A, 671, A61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Pancino, E., Romano, D., Tang, B., et al. 2017a, A&A, 601, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Pancino, E., Lardo, C., Altavilla, G., et al. 2017b, A&A, 598, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Paszke, A., Gross, S., Massa, F., et al. 2019, in Advances in Neural Information Processing Systems 32, eds. H. Wallach, H. Larochelle, A. Beygelzimer, et al. (Curran Associates, Inc.), 8024 [Google Scholar]
- Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
- Pinsonneault, M. H., Elsworth, Y. P., Tayar, J., et al. 2018, ApJS, 239, 32 [Google Scholar]
- Queiroz, A. B. A., Anders, F., Chiappini, C., et al. 2020, A&A, 638, A76 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Randich, S., Gilmore, G., & Gaia-ESO Consortium 2013, The Messenger, 154, 47 [NASA ADS] [Google Scholar]
- Recio-Blanco, A., de Laverny, P., Kordopatis, G., et al. 2014, A&A, 567, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Roberts, D. A., Yaida, S., & Hanin, B. 2022, The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks (Cambridge: Cambridge University Press) [Google Scholar]
- Rosenthal, D. A. 1988, Eur. South. Obs. Conf. Workshop Proc., 28, 245 [NASA ADS] [Google Scholar]
- Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [Google Scholar]
- Steinmetz, M., Matijevič, G., Enke, H., et al. 2020, AJ, 160, 82 [NASA ADS] [CrossRef] [Google Scholar]
- Traven, G., Matijevič, G., Zwitter, T., et al. 2017, ApJS, 228, 24 [NASA ADS] [CrossRef] [Google Scholar]
- Valentini, M., Chiappini, C., Miglio, A., et al. 2016, Astron. Nachr., 337, 970 [Google Scholar]
- Valentini, M., Chiappini, C., Davies, G. R., et al. 2017, A&A, 600, A66 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- van der Maaten, L., & Hinton, G. 2008, J. Mach. Learn. Res., 9, 2579 [Google Scholar]
- Yan, Y., Du, C., Liu, S., et al. 2019, ApJ, 880, 36 [NASA ADS] [CrossRef] [Google Scholar]
- Zhao, G., Zhao, Y.-H., Chu, Y.-Q., Jing, Y.-P., & Deng, L.-C. 2012, RAA, 12, 723 [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.