Tino, Peter ;
Cuevas-Tello, Juan C. ;
Raychaudhury, Somak
Estimating Time Delay in Gravitationally Lensed Fluxes
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.
BibTeX - Entry
@InProceedings{tino_et_al:DSP:2009:2037,
author = {Peter Tino and Juan C. Cuevas-Tello and Somak Raychaudhury},
title = {Estimating Time Delay in Gravitationally Lensed Fluxes},
booktitle = {Similarity-based learning on structures},
year = {2009},
editor = {Michael Biehl and Barbara Hammer and Sepp Hochreiter and Stefan C. Kremer and Thomas Villmann},
number = {09081},
series = {Dagstuhl Seminar Proceedings},
ISSN = {1862-4405},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2009/2037},
annote = {Keywords: Time series, kernel regression, statistical analysis, evolutionary algorithms, mixed representation}
}
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Keywords: |
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Time series, kernel regression, statistical analysis, evolutionary algorithms, mixed representation |
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Seminar: |
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09081 - Similarity-based learning on structures
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Issue date: |
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2009 |
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Date of publication: |
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23.06.2009 |