Multiresolution approach for period determination on unevenly sampled data

X. Otazu; M. Ribó; J. M. Paredes; M. Peracaula; J. Núñez

doi:https://doi.org/10.1111/j.1365-2966.2004.07774.x

Multiresolution approach for period determination on unevenly sampled data

X. Otazu, M. Ribó, J. M. Paredes, M. Peracaula, J. Núñez

Research output: Contribution to journal › Article › Research › peer-review

6 Citations (Scopus)

Abstract

In this paper we present a multiresolution-based method for period determination that is able to deal with unevenly sampled data. This method allows us to detect superimposed periodic signals with lower signal-to-noise ratios than in classical methods. This multiresolution-based method is a generalization of the wavelet-based method for period detection that is unable to deal with unevenly sampled data, presented by the authors in an earlier paper. This new method is a useful tool for the analysis of real data and, in particular, for the analysis of astronomical data.

Original language	English
Pages (from-to)	215-219
Journal	Monthly Notices of the Royal Astronomical Society
Volume	351
DOIs	https://doi.org/10.1111/j.1365-2966.2004.07774.x
Publication status	Published - 11 Jun 2004

Keywords

Methods: data analysis
Methods: numerical
Stars: variables: other
X-rays: binaries

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title = "Multiresolution approach for period determination on unevenly sampled data",

abstract = "In this paper we present a multiresolution-based method for period determination that is able to deal with unevenly sampled data. This method allows us to detect superimposed periodic signals with lower signal-to-noise ratios than in classical methods. This multiresolution-based method is a generalization of the wavelet-based method for period detection that is unable to deal with unevenly sampled data, presented by the authors in an earlier paper. This new method is a useful tool for the analysis of real data and, in particular, for the analysis of astronomical data.",

keywords = "Methods: data analysis, Methods: numerical, Stars: variables: other, X-rays: binaries",

author = "X. Otazu and M. Rib{\'o} and Paredes, {J. M.} and M. Peracaula and J. N{\'u}{\~n}ez",

year = "2004",

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doi = "https://doi.org/10.1111/j.1365-2966.2004.07774.x",

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T1 - Multiresolution approach for period determination on unevenly sampled data

AU - Otazu, X.

AU - Ribó, M.

AU - Paredes, J. M.

AU - Peracaula, M.

AU - Núñez, J.

PY - 2004/6/11

Y1 - 2004/6/11

N2 - In this paper we present a multiresolution-based method for period determination that is able to deal with unevenly sampled data. This method allows us to detect superimposed periodic signals with lower signal-to-noise ratios than in classical methods. This multiresolution-based method is a generalization of the wavelet-based method for period detection that is unable to deal with unevenly sampled data, presented by the authors in an earlier paper. This new method is a useful tool for the analysis of real data and, in particular, for the analysis of astronomical data.

AB - In this paper we present a multiresolution-based method for period determination that is able to deal with unevenly sampled data. This method allows us to detect superimposed periodic signals with lower signal-to-noise ratios than in classical methods. This multiresolution-based method is a generalization of the wavelet-based method for period detection that is unable to deal with unevenly sampled data, presented by the authors in an earlier paper. This new method is a useful tool for the analysis of real data and, in particular, for the analysis of astronomical data.

KW - Methods: data analysis

KW - Methods: numerical

KW - Stars: variables: other

KW - X-rays: binaries

U2 - https://doi.org/10.1111/j.1365-2966.2004.07774.x

DO - https://doi.org/10.1111/j.1365-2966.2004.07774.x

M3 - Article

VL - 351

SP - 215

EP - 219

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

ER -

Multiresolution approach for period determination on unevenly sampled data

Abstract

Keywords

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