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Prediction of Star Formation Rates Using an Artificial Neural Network
Ashraf Ayubinia, Jong-Hak Woo, Fatemeh Hafezianzadeh, Taehwan Kim and Changseok Kim The Astrophysical Journal 980(2) 177 (2025) https://doi.org/10.3847/1538-4357/ada366
Performance Comparison of Supervised Machine Learning Methods in Classifying Celestial Objects
Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
P. A. C. Cunha, A. Humphrey, J. Brinchmann, S. G. Morais, R. Carvajal, J. M. Gomes, I. Matute and A. Paulino-Afonso Astronomy & Astrophysics 687 A269 (2024) https://doi.org/10.1051/0004-6361/202346426
Wide Area VISTA Extra-galactic Survey (WAVES): unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and hdbscan
Todd L Cook, Behnood Bandi, Sam Philipsborn, Jon Loveday, Sabine Bellstedt, Simon P Driver, Aaron S G Robotham, Maciej Bilicki, Gursharanjit Kaur, Elmo Tempel, Ivan Baldry, Daniel Gruen, Marcella Longhetti, Angela Iovino, Benne W Holwerda and Ricardo Demarco Monthly Notices of the Royal Astronomical Society 535(3) 2129 (2024) https://doi.org/10.1093/mnras/stae2389
Supervised star, galaxy, and QSO classification with sharpened dimensionality reduction
Ensemble Learning for Stellar Classification and Radius Estimation from Multimodal Data
Zhi-Jie Deng, Sheng-Yuan Yu, A-Li Luo, Xiao Kong and Xiang-Ru Li Research in Astronomy and Astrophysics 24(11) 115019 (2024) https://doi.org/10.1088/1674-4527/ad86a6
Machine learning based stellar classification with highly sparse photometry data
Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
Andrea Sante, Andreea S Font, Sandra Ortega-Martorell, Ivan Olier and Ian G McCarthy Monthly Notices of the Royal Astronomical Society 531(4) 4363 (2024) https://doi.org/10.1093/mnras/stae1398
Exploring galactic properties with machine learning
A Multimodal Transfer Learning Method for Classifying Images of Celestial Point Sources
Bingjun Wang, Shuxin Hong, Zhiyang Yuan, A-Li Luo, Xiao Kong and Zhiqiang Zou Publications of the Astronomical Society of the Pacific 135(1052) 104502 (2023) https://doi.org/10.1088/1538-3873/acfbb9
J-PLUS: galaxy-star-quasar classification for DR3
R von Marttens, V Marra, M Quartin, L Casarini, P O Baqui, A Alvarez-Candal, F J Galindo-Guil, J A Fernández-Ontiveros, Andrés del Pino, L A Díaz-García, C López-Sanjuan, J Alcaniz, R Angulo, A J Cenarro, D Cristóbal-Hornillos, R Dupke, A Ederoclite, C Hernández-Monteagudo, A Marín-Franch, M Moles, L Sodré, J Varela and H Vázquez Ramió Monthly Notices of the Royal Astronomical Society 527(2) 3347 (2023) https://doi.org/10.1093/mnras/stad3373
Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars
Fatemeh Zahra Zeraatgari, Fatemeh Hafezianzadeh, Yanxia Zhang, Liquan Mei, Ashraf Ayubinia, Amin Mosallanezhad and Jingyi Zhang Monthly Notices of the Royal Astronomical Society 527(3) 4677 (2023) https://doi.org/10.1093/mnras/stad3436
Selection of powerful radio galaxies with machine learning
R. Carvajal, I. Matute, J. Afonso, R. P. Norris, K. J. Luken, P. Sánchez-Sáez, P. A. C. Cunha, A. Humphrey, H. Messias, S. Amarantidis, D. Barbosa, H. A. Cruz, H. Miranda, A. Paulino-Afonso and C. Pappalardo Astronomy & Astrophysics 679 A101 (2023) https://doi.org/10.1051/0004-6361/202245770
Photometric classification of quasars from ALHAMBRA survey using random forest
Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
A Humphrey, P A C Cunha, A Paulino-Afonso, et al. Monthly Notices of the Royal Astronomical Society 520(1) 305 (2023) https://doi.org/10.1093/mnras/stac3596
Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
Đorđe V. Savić, Isidora Jankov, Weixiang Yu, Vincenzo Petrecca, Matthew J. Temple, Qingling Ni, Raphael Shirley, Andjelka B. Kovačević, Mladen Nikolić, Dragana Ilić, Luka Č. Popović, Maurizio Paolillo, Swayamtrupta Panda, Aleksandra Ćiprijanović and Gordon T. Richards The Astrophysical Journal 953(2) 138 (2023) https://doi.org/10.3847/1538-4357/ace31a
Beyond the Local Volume. I. Surface Densities of Ultracool Dwarfs in Deep HST/WFC3 Parallel Fields
Christian Aganze, Adam J. Burgasser, Mathew Malkan, Christopher A. Theissen, Roberto A. Tejada Arevalo, Chih-Chun Hsu, Daniella C. Bardalez Gagliuffi, Russell E. Ryan and Benne Holwerda The Astrophysical Journal 924(2) 114 (2022) https://doi.org/10.3847/1538-4357/ac35ea
Applying Random Forest Classification to Ultracool Dwarf Discovery in Deep Surveys. I. Color Classification with SDSS, UKIDSS, and WISE Photometry
Zijie Gong, Adriana Nava Vega, Eduardo Gauna Gutierrez, Arantxa Mendiola Maytorena, Carlos Verdaguer, Christian Aganze, Christopher Danner and Adam J. Burgasser Research Notes of the AAS 6(4) 74 (2022) https://doi.org/10.3847/2515-5172/ac6521
Data mining techniques on astronomical spectra data – II. Classification analysis
Haifeng Yang, Lichan Zhou, Jianghui Cai, et al. Monthly Notices of the Royal Astronomical Society 518(4) 5904 (2022) https://doi.org/10.1093/mnras/stac3292
Photometric identification of compact galaxies, stars, and quasars using multiple neural networks
Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, Kaushal Sharma, M Vivek and Ajit Kembhavi Monthly Notices of the Royal Astronomical Society 518(2) 3123 (2022) https://doi.org/10.1093/mnras/stac3336
A new automated tool for the spectral classification of OB stars
Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth
A Humphrey, W Kuberski, J Bialek, N Perrakis, W Cools, N Nuyttens, H Elakhrass and P A C Cunha Monthly Notices of the Royal Astronomical Society: Letters 517(1) L116 (2022) https://doi.org/10.1093/mnrasl/slac120
An exploration of how training set composition bias in machine learning affects identifying rare objects
Shruthi Srinivasaprasad Lecture Notes on Data Engineering and Communications Technologies, Congress on Intelligent Systems 111 667 (2022) https://doi.org/10.1007/978-981-16-9113-3_49
Practical galaxy morphology tools from deep supervised representation learning
Mike Walmsley, Anna M M Scaife, Chris Lintott, et al. Monthly Notices of the Royal Astronomical Society 513(2) 1581 (2022) https://doi.org/10.1093/mnras/stac525
The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data
Abdurro’uf, Katherine Accetta, Conny Aerts, Víctor Silva Aguirre, Romina Ahumada, Nikhil Ajgaonkar, N. Filiz Ak, Shadab Alam, Carlos Allende Prieto, Andrés Almeida, Friedrich Anders, Scott F. Anderson, Brett H. Andrews, Borja Anguiano, Erik Aquino-Ortíz, Alfonso Aragón-Salamanca, Maria Argudo-Fernández, Metin Ata, Marie Aubert, Vladimir Avila-Reese, Carles Badenes, Rodolfo H. Barbá, Kat Barger, Jorge K. Barrera-Ballesteros, Rachael L. Beaton, et al. The Astrophysical Journal Supplement Series 259(2) 35 (2022) https://doi.org/10.3847/1538-4365/ac4414
Deep learning applications based on SDSS photometric data: detection and classification of sources
On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2
L Nakazono, C Mendes de Oliveira, N S T Hirata, et al. Monthly Notices of the Royal Astronomical Society 507(4) 5847 (2021) https://doi.org/10.1093/mnras/stab1835
The regression of effective temperatures in APOGEE and LAMOST
Discovery of five new Galactic symbiotic stars in the VPHAS+ survey
Stavros Akras, Denise R Gonçalves, Alvaro Alvarez-Candal and Claudio B Pereira Monthly Notices of the Royal Astronomical Society 502(2) 2513 (2021) https://doi.org/10.1093/mnras/stab195
A diffusion-based method for removing background stars from astronomical images
Identification of BASS DR3 sources as stars, galaxies, and quasars by XGBoost
Changhua Li, Yanxia Zhang, Chenzhou Cui, et al. Monthly Notices of the Royal Astronomical Society 506(2) 1651 (2021) https://doi.org/10.1093/mnras/stab1650
Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
Attention-gating for improved radio galaxy classification
Micah Bowles, Anna M M Scaife, Fiona Porter, Hongming Tang and David J Bastien Monthly Notices of the Royal Astronomical Society 501(3) 4579 (2021) https://doi.org/10.1093/mnras/staa3946
Deep Learning assessment of galaxy morphology in S-PLUS Data Release 1