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Data science methodology for time-delay estimation and data preprocessing of the time-delay challenge
Luis Leon-Anaya, Juan C Cuevas-Tello, Octavio Valenzuela, César A Puente and Carlos Soubervielle-Montalvo Monthly Notices of the Royal Astronomical Society 522(1) 1323 (2023) https://doi.org/10.1093/mnras/stad817
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
Detecting Exoplanet Transits through Machine-learning Techniques with Convolutional Neural Networks
Pattana Chintarungruangchai and Ing-Guey Jiang Publications of the Astronomical Society of the Pacific 131(1000) 064502 (2019) https://doi.org/10.1088/1538-3873/ab13d3
Revealing the non-linear behaviour of the lensed quasar Q0957+561
A Bewketu Belete, B L Canto Martins, I C Leão and J R De Medeiros Monthly Notices of the Royal Astronomical Society 484(3) 3552 (2019) https://doi.org/10.1093/mnras/stz203
A simple method to determine time delays in the presence of microlensing: application to HE 0435–1112 and PG 1115+080
V. S. Tsvetkova, V. M. Shulga and L. A. Berdina Monthly Notices of the Royal Astronomical Society 461(4) 3714 (2016) https://doi.org/10.1093/mnras/stw1540
Intelligent Data Engineering and Automated Learning – IDEAL 2016
Sultanah Al Otaibi, Peter Tiňo and Somak Raychaudhury Lecture Notes in Computer Science, Intelligent Data Engineering and Automated Learning – IDEAL 2016 9937 552 (2016) https://doi.org/10.1007/978-3-319-46257-8_59
Kernel regression estimates of time delays between gravitationally lensed fluxes
Sultanah AL Otaibi, Peter Tiňo, Juan C. Cuevas-Tello, Ilya Mandel and Somak Raychaudhury Monthly Notices of the Royal Astronomical Society 459(1) 573 (2016) https://doi.org/10.1093/mnras/stw510
GERLUMPH DATA RELEASE 2:2.5 BILLION SIMULATED MICROLENSING LIGHT CURVES