The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
A&A, 644 (2020) A168
Published online: 2020-12-16
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
CNN-derived elemental abundances of LAMOST DR10 giants: implications for galactic substructures
Haoyang Liu, Cuihua Du, Mingji Deng and Jian ZhangMonthly Notices of the Royal Astronomical Society 541 (1) 58 (2025)
https://doi.org/10.1093/mnras/staf964
Impact of ⟨3D⟩ non-local thermodynamic equilibrium on the Galactic chemical evolution of oxygen with the Radial Velocity Experiment
G. GuiglionAstronomy & Astrophysics 699 A191 (2025)
https://doi.org/10.1051/0004-6361/202453526
Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies
Kurt Walsen, Paula Jofré, Sven Buder, Keaghan Yaxley, Payel Das, Robert M Yates, Xia Hua, Theosamuele Signor, Camilla Eldridge, Alvaro Rojas-Arriagada, Patricia B Tissera, Evelyn Johnston, Claudia Aguilera-Gómez, Manuela Zoccali, Gerry Gilmore and Robert FoleyMonthly Notices of the Royal Astronomical Society 529 (3) 2946 (2024)
https://doi.org/10.1093/mnras/stae280
Stellar atmospheric parameters from Gaia BP/RP spectra using uncertain neural networks
Connor P Fallows and Jason L SandersMonthly Notices of the Royal Astronomical Society 531 (1) 2126 (2024)
https://doi.org/10.1093/mnras/stae1303
Beyond Gaia DR3: Tracing the [α/M] – [M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks
G. Guiglion, S. Nepal, C. Chiappini, S. Khoperskov, G. Traven, A. B. A. Queiroz, M. Steinmetz, M. Valentini, Y. Fournier, A. Vallenari, K. Youakim, M. Bergemann, S. Mészáros, S. Lucatello, R. Sordo, S. Fabbro, I. Minchev, G. Tautvaišienė, Š. Mikolaitis and J. MontalbánAstronomy & Astrophysics 682 A9 (2024)
https://doi.org/10.1051/0004-6361/202347122
Revisiting the membership, multiplicity, and age of the Beta Pictoris Moving Group in the Gaia era
Rena A Lee, Eric Gaidos, Jennifer van Saders, Gregory A Feiden and Jonathan GagnéMonthly Notices of the Royal Astronomical Society 528 (3) 4760 (2024)
https://doi.org/10.1093/mnras/stae007
Spectroscopic analysis of hot, massive stars in large spectroscopic surveys with de-idealized models
J M Bestenlehner, T Enßlin, M Bergemann, P A Crowther, M Greiner and M SeligMonthly Notices of the Royal Astronomical Society 528 (4) 6735 (2024)
https://doi.org/10.1093/mnras/stae298
Atmospheric parameters and chemical abundances within 100 pc: a sample of G, K, and M main-sequence stars
Ricardo López-Valdivia, Lucía Adame, Eduardo Zagala Lagunas, Carlos G Román-Zúñiga, Jesús Hernández, Edilberto Sánchez, José G Fernández-Trincado, Leticia Carigi, Marina Kounkel, Richard R Lane, Keivan G Stassun and Sandro VillanovaMonthly Notices of the Royal Astronomical Society 533 (1) 395 (2024)
https://doi.org/10.1093/mnras/stae1809
Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost
A. Khalatyan, F. Anders, C. Chiappini, A. B. A. Queiroz, S. Nepal, M. dal Ponte, C. Jordi, G. Guiglion, M. Valentini, G. Torralba Elipe, M. Steinmetz, M. Pantaleoni-González, S. Malhotra, Ó. Jiménez-Arranz, H. Enke, L. Casamiquela and J. ArdèvolAstronomy & Astrophysics 691 A98 (2024)
https://doi.org/10.1051/0004-6361/202451427
Inferring stellar parameters and their uncertainties from high-resolution spectroscopy using invertible neural networks
N. Candebat, G. G. Sacco, L. Magrini, F. Belfiore, M. Van der Swaelmen and S. ZibettiAstronomy & Astrophysics 692 A228 (2024)
https://doi.org/10.1051/0004-6361/202451251
ESNet: Estimating Stellar Parameters from LAMOST Low-Resolution Stellar Spectra
Kun Wang, Bo Qiu, A-li Luo, Fuji Ren and Xia JiangUniverse 9 (9) 416 (2023)
https://doi.org/10.3390/universe9090416
Stellar Parameters and Chemical Abundances Estimated from LAMOST-II DR8 MRS Based on Cycle-StarNet
Rui Wang, A-Li Luo, Shuo Zhang, Yuan-Sen Ting and Teaghan O’BriainThe Astrophysical Journal Supplement Series 266 (2) 40 (2023)
https://doi.org/10.3847/1538-4365/acce36
The Gaia-ESO Survey: Preparing the ground for 4MOST and WEAVE galactic surveys
S. Nepal, G. Guiglion, R. S. de Jong, et al.Astronomy & Astrophysics 671 A61 (2023)
https://doi.org/10.1051/0004-6361/202244765
The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with machine learning
M. Ambrosch, G. Guiglion, Š. Mikolaitis, et al.Astronomy & Astrophysics 672 A46 (2023)
https://doi.org/10.1051/0004-6361/202244766
StarUnLink: identifying and mitigating signals from communication satellites in stellar spectral surveys
Spencer Bialek, Sara Lucatello, Sebastien Fabbro, Kwang Moo Yi and Kim A VennMonthly Notices of the Royal Astronomical Society 524 (1) 529 (2023)
https://doi.org/10.1093/mnras/stad1889
Assessment of [Fe/H] determinations for FGK stars in spectroscopic surveys
C. Soubiran, N. Brouillet and L. CasamiquelaAstronomy & Astrophysics 663 A4 (2022)
https://doi.org/10.1051/0004-6361/202142409
Recovery of TESS Stellar Rotation Periods Using Deep Learning
Zachary R. Claytor, Jennifer L. van Saders, Joe Llama, Peter Sadowski, Brandon Quach and Ellis A. AvalloneThe Astrophysical Journal 927 (2) 219 (2022)
https://doi.org/10.3847/1538-4357/ac498f
The Spectroscopic Binaries from the LAMOST Medium-resolution Survey. I. Searching for Double-lined Spectroscopic Binaries with a Convolutional Neural Network
Bo Zhang, Ying-Jie Jing, Fan Yang, Jun-Chen Wan, Xin Ji, Jian-Ning Fu, Chao Liu, Xiao-Bin Zhang, Feng Luo, Hao Tian, Yu-Tao Zhou, Jia-Xin Wang, Yan-Jun Guo, Weikai Zong, Jian-Ping Xiong and Jiao LiThe Astrophysical Journal Supplement Series 258 (2) 26 (2022)
https://doi.org/10.3847/1538-4365/ac42d1
APOGEE Net: An Expanded Spectral Model of Both Low-mass and High-mass Stars
Dani Sprague, Connor Culhane, Marina Kounkel, Richard Olney, K. R. Covey, Brian Hutchinson, Ryan Lingg, Keivan G. Stassun, Carlos G. Román-Zúñiga, Alexandre Roman-Lopes, David Nidever, Rachael L. Beaton, Jura Borissova, Amelia Stutz, Guy S. Stringfellow, Karla Peña Ramírez, Valeria Ramírez-Preciado, Jesús Hernández, Jinyoung Serena Kim and Richard R. LaneThe Astronomical Journal 163 (4) 152 (2022)
https://doi.org/10.3847/1538-3881/ac4de7
Deep learning application for stellar parameters determination: I-constraining the hyperparameters
Marwan Gebran, Kathleen Connick, Hikmat Farhat, Frédéric Paletou and Ian BentleyOpen Astronomy 31 (1) 38 (2022)
https://doi.org/10.1515/astro-2022-0007
Cycle-StarNet: Bridging the Gap between Theory and Data by Leveraging Large Data Sets
Teaghan O’Briain, Yuan-Sen 源 森 Ting 丁, Sébastien Fabbro, Kwang M. Yi, Kim Venn and Spencer BialekThe Astrophysical Journal 906 (2) 130 (2021)
https://doi.org/10.3847/1538-4357/abca96
The Swan: Data-driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves
Maryum Sayeed, Daniel Huber, Adam Wheeler and Melissa K. NessThe Astronomical Journal 161 (4) 170 (2021)
https://doi.org/10.3847/1538-3881/abdf4c
The distribution of [α/Fe] in the Milky Way disc
Fiorenzo Vincenzo, David H Weinberg, Andrea Miglio, Richard R Lane and Alexandre Roman-LopesMonthly Notices of the Royal Astronomical Society 508 (4) 5903 (2021)
https://doi.org/10.1093/mnras/stab2899
TITANS metal-poor reference stars
R. E. Giribaldi, A. R. da Silva, R. Smiljanic and D. Cornejo EspinozaAstronomy & Astrophysics 650 A194 (2021)
https://doi.org/10.1051/0004-6361/202140751