EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 693 (January 2025) A&A, 693 (2025) A300 References
Open Access
Issue
A&A
Volume 693, January 2025
Article Number A300
Number of page(s) 17
Section Numerical methods and codes
DOI https://doi.org/10.1051/0004-6361/202451309
Published online 27 January 2025
  1. Abdurro’uf, Accetta, K., Aerts, C., et al. 2022, ApJS, 259, 35 [NASA ADS] [CrossRef] [Google Scholar]
  2. Bailer-Jones, C. A. L., Irwin, M., & Von Hippel, T. 1998, MNRAS, 298, 361 [NASA ADS] [CrossRef] [Google Scholar]
  3. Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
  4. Brice, M. J., & Andonie, R. 2019, AJ, 158, 188 [NASA ADS] [CrossRef] [Google Scholar]
  5. Chen, Y.-P., Yan, R., Maraston, C., et al. 2020, ApJ, 899, 62 [Google Scholar]
  6. Cohen, J. 2013, Statistical Power Analysis for the Behavioral Sciences (Burlington: Elsevier Science) [CrossRef] [Google Scholar]
  7. Danka, T., & Horvath, P. 2018, arXiv e-prints [arXiv:1805.00979] [Google Scholar]
  8. Dimitrakakis, C., & Savu-Krohn, C. 2008, Cost-Minimising Strategies for Data Labelling: Optimal Stopping and Active Learning (Springer Berlin Heidelberg), 96 [Google Scholar]
  9. Drory, N., MacDonald, N., Bershady, M. A., et al. 2015, AJ, 149, 77 [CrossRef] [Google Scholar]
  10. Dupree, A. K., Avrett, E. H., & Kurucz, R. L. 2016, ApJ, 821, L7 [Google Scholar]
  11. Fabbro, S., Venn, K. A., O’Briain, T., et al. 2017, MNRAS, 475, 2978 [Google Scholar]
  12. Friedman, J. H. 2001, Ann. Stat., 29, 1189 [Google Scholar]
  13. Ghahramani, Z., Gal, Y., & Islam, R. 2017, Deep Bayesian Active Learning with Image Data [Google Scholar]
  14. Ghazaryan, S., Alecian, G., & Hakobyan, A. A. 2018, MNRAS, 480, 2953 [NASA ADS] [CrossRef] [Google Scholar]
  15. Ghazaryan, S., Alecian, G., & Hakobyan, A. A. 2019, MNRAS, 487, 5922 [NASA ADS] [CrossRef] [Google Scholar]
  16. Giridhar, S. 2010, Spectral Classification: Old and Contemporary (Berlin, Heidelberg: Springer), 165 [Google Scholar]
  17. Gray, R. O. 2009, Stellar Spectral Classification, eds. C. J. Corbally, & A. J. Burgasser, Princeton series in astrophysics (Princeton: Princeton University Press) [Google Scholar]
  18. Gray, R. O., & Corbally, C. J. 2014, AJ, 147, 80 [CrossRef] [Google Scholar]
  19. Greenacre, M., Groenen, P. J. F., Hastie, T., et al. 2022, Nat. Rev. Methods Primers, 2, 100 [CrossRef] [Google Scholar]
  20. Gulati, R. K., Gupta, R., Gothoskar, P., & Khobragade, S. 1994, ApJ, 426, 340 [CrossRef] [Google Scholar]
  21. Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332 [NASA ADS] [CrossRef] [Google Scholar]
  22. Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357 [NASA ADS] [CrossRef] [Google Scholar]
  23. Hill, L., Thomas, D., Maraston, C., et al. 2021, MNRAS, 509, 4308 [NASA ADS] [CrossRef] [Google Scholar]
  24. Hill, L., Thomas, D., Maraston, C., et al. 2022, MNRAS, 517, 4275 [Google Scholar]
  25. Huang, S.-J., Jin, R., & Zhou, Z.-H. 2014, IEEE Trans. Pattern Anal. Mach. Intell., 36, 1936 [Google Scholar]
  26. Hunter, J. D. 2007, Comput. Sci. Eng., 9, 90 [NASA ADS] [CrossRef] [Google Scholar]
  27. Imig, J., Holtzman, J. A., Yan, R., et al. 2022, AJ, 163, 56 [NASA ADS] [CrossRef] [Google Scholar]
  28. Ishida, E. E. O., Beck, R., González-Gaitán, S., et al. 2018, MNRAS, 483, 2 [Google Scholar]
  29. Ishida, E. E. O., Kornilov, M. V., Malanchev, K. L., et al. 2021, A&A, 650, A195 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Ivezić, Ž., Connolly, A. J., VanderPlas, J. T., & Gray, A. 2020, Statistics, Data Mining, and Machine Learning in Astronomy, eds. A. J. Connolly, J. VanderPlas, A. Gray, & J. T. Vanderplas (Princeton: Princeton University Press) [Google Scholar]
  31. Kesseli, A. Y., West, A. A., Veyette, M., et al. 2017, ApJS, 230, 16 [NASA ADS] [CrossRef] [Google Scholar]
  32. Kullback, S., & Leibler, R. A. 1951, Ann. Math. Statist., 22, 79 [Google Scholar]
  33. Kurucz, R. L. 2011, Can. J. Phys., 89, 417 [Google Scholar]
  34. Lazarz, D., Yan, R., Wilhelm, R., et al. 2022, A&A, 668, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Lemaître, G., Nogueira, F., & Aridas, C. K. 2017, JMLR, 18, 1 [Google Scholar]
  36. Li, K., Li, G., Wang, Y., et al. 2021, in 2021 IEEE 37th International Conference on Data Engineering (ICDE) (IEEE) [Google Scholar]
  37. Lintott, C., Schawinski, K., Bamford, S., et al. 2010, MNRAS, 410, 166 [Google Scholar]
  38. Lughofer, E. 2012, Pattern Recognit., 45, 884 [NASA ADS] [CrossRef] [Google Scholar]
  39. Manteiga, M., Carricajo, I., Rodríguez, A., Dafonte, C., & Arcay, B. 2009, AJ, 137, 3245 [NASA ADS] [CrossRef] [Google Scholar]
  40. Marsland, S. 2014, Machine Learning: An Algorithmic Perspective (London: Chapman and Hall/CRC) [Google Scholar]
  41. McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, SciPy (SciPy) [Google Scholar]
  42. Pandas Development Team 2024, https://doi.org/10.5281/ZENODO.3509134 [Google Scholar]
  43. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, JMLR, 12, 2825 [Google Scholar]
  44. Price-Whelan, A. M., Sipocz, B. M., Günther, H. M., et al. 2018, AJ, 156, 123 [NASA ADS] [CrossRef] [Google Scholar]
  45. Price-Whelan, A. M., Lim, P. L., Earl, N., et al. 2022, ApJ, 935, 167 [NASA ADS] [CrossRef] [Google Scholar]
  46. Richards, J. W., Homrighausen, D., Freeman, P. E., Schafer, C. M., & Poznanski, D. 2011, MNRAS, 419, 1121 [Google Scholar]
  47. Robitaille, T. P., Tollerud, E. J., Greenfield, P., et al. 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Santos, N. C., Israelian, G., & Mayor, M. 2004, A&A, 415, 1153 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Settles, B. 2012, Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning (San Rafael, USA: Morgan & Claypool), 18 [Google Scholar]
  50. Sharma, K., Kembhavi, A., Kembhavi, A., et al. 2019, MNRAS, 491, 2280 [Google Scholar]
  51. Singh, H. P., Gulati, R. K., & Gupta, R. 1998, MNRAS, 295, 312 [NASA ADS] [CrossRef] [Google Scholar]
  52. Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32 [Google Scholar]
  53. Solorio, T., Fuentes, O., Terlevich, R., & Terlevich, E. 2005, MNRAS, 363, 543 [Google Scholar]
  54. Song, J., Wang, H., Gao, Y., & An, B. 2018, KBS, 159, 244 [Google Scholar]
  55. Tharwat, A., & Schenck, W. 2023, Mathematics, 11, 820 [Google Scholar]
  56. Van Rossum, G. 2020, The Python Library Reference, release 3.8.2 (USA: Python Software Foundation) [Google Scholar]
  57. Walmsley, M., Smith, L., Lintott, C., et al. 2019, MNRAS, 491, 1554 [Google Scholar]
  58. Wilcoxon, F. 1992, Individual Comparisons by Ranking Methods (New York: Springer), 196–202 [Google Scholar]
  59. Yan, R., Chen, Y., Lazarz, D., et al. 2019, ApJ, 883, 175 [Google Scholar]
  60. Zeraatgari, F. Z., Hafezianzadeh, F., Zhang, Y., et al. 2023, MNRAS, 527, 4677 [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.