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The First Catalog of Candidate White Dwarf–Main-sequence Binaries in Open Star Clusters: A New Window into Common Envelope Evolution
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Improved source classification and performance analysis using Gaia DR3
Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data
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Galaxy image classification using hierarchical data learning with weighted sampling and label smoothing
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Spectral Energy Distributions in Three Deep-drilling Fields of the Vera C. Rubin Observatory Legacy Survey of Space and Time: Source Classification and Galaxy Properties
Fan Zou, W. N. Brandt, Chien-Ting Chen, Joel Leja, Qingling Ni, Wei Yan, Guang Yang, Shifu Zhu, Bin Luo, Kristina Nyland, Fabio Vito and Yongquan Xue The Astrophysical Journal Supplement Series 262(1) 15 (2022) https://doi.org/10.3847/1538-4365/ac7bdf
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A multi-angle comprehensive solution based on deep learning to extract cultivated land information from high-resolution remote sensing images
Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey
Wei Wei, E A Huerta, Bradley C Whitmore, et al. Monthly Notices of the Royal Astronomical Society 493(3) 3178 (2020) https://doi.org/10.1093/mnras/staa325
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Active galactic nucleus selection in the AKARI NEP-Deep field with the fuzzy support vector machine algorithm
Artem Poliszczuk, Aleksandra Solarz, Agnieszka Pollo, Maciej Bilicki, Tsutomu T Takeuchi, Hideo Matsuhara, Tomotsugu Goto, Toshinobu Takagi, Takehiko Wada, Yoichi Ohyama, Hitoshi Hanami, Takamitsu Miyaji, Nagisa Oi, Matthew Malkan, Kazumi Murata, Helen Kim and Jorge Díaz Tello Publications of the Astronomical Society of Japan 71(3) (2019) https://doi.org/10.1093/pasj/psz043
Identification of Young Stellar Object candidates in the Gaia DR2 x AllWISE catalogue with machine learning methods
G Marton, P Ábrahám, E Szegedi-Elek, et al. Monthly Notices of the Royal Astronomical Society 487(2) 2522 (2019) https://doi.org/10.1093/mnras/stz1301
A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
Fang Xia An, S. M. Stach, Ian Smail, A. M. Swinbank, O. Almaini, C. Simpson, W. Hartley, D. T. Maltby, R. J. Ivison, V. Arumugam, J. L. Wardlow, E. A. Cooke, B. Gullberg, A. P. Thomson, Chian-Chou Chen, J. M. Simpson, J. E. Geach, D. Scott, J. S. Dunlop, D. Farrah, P. van der Werf, A. W. Blain, C. Conselice, M. Michałowski, S. C. Chapman and K. E. K. Coppin The Astrophysical Journal 862(2) 101 (2018) https://doi.org/10.3847/1538-4357/aacdaa
The Dark Energy Survey: Data Release 1
T. M. C. Abbott, F. B. Abdalla, S. Allam, A. Amara, J. Annis, J. Asorey, S. Avila, O. Ballester, M. Banerji, W. Barkhouse, L. Baruah, M. Baumer, K. Bechtol, M. R. Becker, A. Benoit-Lévy, G. M. Bernstein, E. Bertin, J. Blazek, S. Bocquet, D. Brooks, D. Brout, E. Buckley-Geer, D. L. Burke, V. Busti, R. Campisano, et al. The Astrophysical Journal Supplement Series 239(2) 18 (2018) https://doi.org/10.3847/1538-4365/aae9f0
Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars
Giuseppe Morello, P. W. Morris, S. D. Van Dyk, A. P. Marston and J. C. Mauerhan Monthly Notices of the Royal Astronomical Society 473(2) 2565 (2018) https://doi.org/10.1093/mnras/stx2474
Analysis of a custom support vector machine for photometric redshift estimation and the inclusion of galaxy shape information
Mapping the Cosmic Web with the largest all-sky surveys
Maciej Bilicki, John A. Peacock, Thomas H. Jarrett, Michelle E. Cluver and Louise Steward Proceedings of the International Astronomical Union 11(S308) 143 (2014) https://doi.org/10.1017/S1743921316009753
The VIMOS Public Extragalactic Redshift Survey (VIPERS)