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Home All issues A&A, 454 3 (2006) 695-706 Article references

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Cited article:

Data science methodology for time-delay estimation and data preprocessing of the time-delay challenge

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A survey on machine learning based light curve analysis for variable astronomical sources

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Detecting Exoplanet Transits through Machine-learning Techniques with Convolutional Neural Networks

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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
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A simple method to determine time delays in the presence of microlensing: application to HE 0435–1112 and PG 1115+080

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https://doi.org/10.1093/mnras/stw1540

Intelligent Data Engineering and Automated Learning – IDEAL 2016

Sultanah Al Otaibi, Peter Tiňo and Somak Raychaudhury
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https://doi.org/10.1007/978-3-319-46257-8_59

Kernel regression estimates of time delays between gravitationally lensed fluxes

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GERLUMPH DATA RELEASE 2:2.5 BILLION SIMULATED MICROLENSING LIGHT CURVES

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Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data

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MEASURING THE TIME DELAYS IN GRAVITATIONALLY LENSED QUASARS IN THE PRESENCE OF MICROLENSING

V. S. Tsvetkova, V. M. Shulga and L. A. Berdina
Radio physics and radio astronomy 19 (4) 307 (2014)
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Pushing for the Extreme: Estimation of Poisson Distribution from Low Count Unreplicated Data—How Close Can We Get?

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Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application

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Machine Learning: ECML 2006

Juan C. Cuevas-Tello, Peter Tiňo and Somak Raychaudhury
Lecture Notes in Computer Science, Machine Learning: ECML 2006 4212 614 (2006)
https://doi.org/10.1007/11871842_59