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YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) – II. Using self-organizing maps to explore young star variability in sectors 1–13 of TESS data
Matthew P Battley, David J Armstrong and Don Pollacco Monthly Notices of the Royal Astronomical Society 511(3) 4285 (2022) https://doi.org/10.1093/mnras/stac278
TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data
J. Audenaert, J. S. Kuszlewicz, R. Handberg, A. Tkachenko, D. J. Armstrong, M. Hon, R. Kgoadi, M. N. Lund, K. J. Bell, L. Bugnet, D. M. Bowman, C. Johnston, R. A. García, D. Stello, L. Molnár, E. Plachy, D. Buzasi and C. Aerts The Astronomical Journal 162(5) 209 (2021) https://doi.org/10.3847/1538-3881/ac166a
Classification of OGLE Eclipsing Binary Stars Based on Their Morphology Type with Locally Linear Embedding
Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
P. Sánchez-Sáez, I. Reyes, C. Valenzuela, F. Förster, S. Eyheramendy, F. Elorrieta, F. E. Bauer, G. Cabrera-Vives, P. A. Estévez, M. Catelan, G. Pignata, P. Huijse, D. De Cicco, P. Arévalo, R. Carrasco-Davis, J. Abril, R. Kurtev, J. Borissova, J. Arredondo, E. Castillo-Navarrete, D. Rodriguez, D. Ruz-Mieres, A. Moya, L. Sabatini-Gacitúa, C. Sepúlveda-Cobo and E. Camacho-Iñiguez The Astronomical Journal 161(3) 141 (2021) https://doi.org/10.3847/1538-3881/abd5c1
A survey on machine learning based light curve analysis for variable astronomical sources
Ce Yu, Kun Li, Yanxia Zhang, Jian Xiao, Chenzhou Cui, Yihan Tao, Shanjiang Tang, Chao Sun and Chongke Bi WIREs Data Mining and Knowledge Discovery 11(5) (2021) https://doi.org/10.1002/widm.1425
Variability, periodicity, and contact binaries in WISE
Evan Petrosky, Hsiang-Chih Hwang, Nadia L Zakamska, Vedant Chandra and Matthew J Hill Monthly Notices of the Royal Astronomical Society 503(3) 3975 (2021) https://doi.org/10.1093/mnras/stab592
Deep multi-survey classification of variable stars
Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
Gautham Narayan, Tayeb Zaidi, Monika D. Soraisam, Zhe Wang, Michelle Lochner, Thomas Matheson, Abhijit Saha, Shuo Yang, Zhenge Zhao, John Kececioglu, Carlos Scheidegger, Richard T. Snodgrass, Tim Axelrod, Tim Jenness, Robert S. Maier, Stephen T. Ridgway, Robert L. Seaman, Eric Michael Evans, Navdeep Singh, Clark Taylor, Jackson Toeniskoetter, Eric Welch and Songzhe Zhu The Astrophysical Journal Supplement Series 236(1) 9 (2018) https://doi.org/10.3847/1538-4365/aab781
Machine learning techniques to select Be star candidates
Variable classification in the LSST era: exploring a model for quasi-periodic light curves
J. C. Zinn, C. S. Kochanek, S. Kozłowski, et al. Monthly Notices of the Royal Astronomical Society 468(2) 2189 (2017) https://doi.org/10.1093/mnras/stx586
Wider pulsation instability regions forβCephei and SPB stars calculated using new Los Alamos opacities
Przemysław Walczak, Christopher J. Fontes, James Colgan, David P. Kilcrease and Joyce A. Guzik Astronomy & Astrophysics 580 L9 (2015) https://doi.org/10.1051/0004-6361/201526824
THE EB FACTORY PROJECT. I. A FAST, NEURAL-NET-BASED, GENERAL PURPOSE LIGHT CURVE CLASSIFIER OPTIMIZED FOR ECLIPSING BINARIES
Minjeong Park, Donghoh Kim, Sinsup Cho and Hee-Seok Oh Communications for Statistical Applications and Methods 20(4) 271 (2013) https://doi.org/10.5351/CSAM.2013.20.4.271
Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
J. S. Bloom, J. W. Richards, P. E. Nugent, et al. Publications of the Astronomical Society of the Pacific 124(921) 1175 (2012) https://doi.org/10.1086/668468
Advances in Machine Learning and Data Mining for Astronomy
JOSHUA BLOOM and JOSEPH RICHARDS Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Advances in Machine Learning and Data Mining for Astronomy 20124949 (2012) https://doi.org/10.1201/b11822-10
The Impact of CoRoT and Kepler on Eclipsing Binary Science
Carla Maceroni, Davide Gandolfi, Josefina Montalbán and Conny Aerts Proceedings of the International Astronomical Union 7(S282) 41 (2011) https://doi.org/10.1017/S1743921311026834
ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA
STRESS – STEREO TRansiting Exoplanet and Stellar Survey
Vinothini Sangaralingam, Ian R. Stevens, Steve Spreckley and Jonas Debosscher Proceedings of the International Astronomical Union 5(S264) 434 (2009) https://doi.org/10.1017/S174392130999305X
Automated supervised classification of variable stars in the CoRoT
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