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Estimating Time Delay in Gravitationally Lensed Fluxes

Authors: Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury

Published in: Dagstuhl Seminar Proceedings, Volume 9081, Similarity-based learning on structures (2009)

Abstract

We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. We test the algorithm on several artificial data sets, and also on real astronomical observations. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.

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Peter Tino, Juan C. Cuevas-Tello, and Somak Raychaudhury. Estimating Time Delay in Gravitationally Lensed Fluxes. In Similarity-based learning on structures. Dagstuhl Seminar Proceedings, Volume 9081, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{tino_et_al:DagSemProc.09081.5,
  author =	{Tino, Peter and Cuevas-Tello, Juan C. and Raychaudhury, Somak},
  title =	{{Estimating Time Delay in Gravitationally Lensed Fluxes}},
  booktitle =	{Similarity-based learning on structures},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9081},
  editor =	{Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09081.5},
  URN =		{urn:nbn:de:0030-drops-20378},
  doi =		{10.4230/DagSemProc.09081.5},
  annote =	{Keywords: Time series, kernel regression, statistical analysis, evolutionary algorithms, mixed representation}
}

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Estimating Time Delay in Gravitationally Lensed Fluxes

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