Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Olspert, N. Pelt, J. Käpylä, M. J. Lehtinen, J. J. 2018-06-18T09:21:19Z 2018-06-18T09:21:19Z 2018-07
dc.identifier.citation Olspert , N , Pelt , J , Käpylä , M J & Lehtinen , J J 2018 , ' Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend ' ASTRONOMY AND ASTROPHYSICS , pp. 1-13 . DOI: 10.1051/0004-6361/201732524 en
dc.identifier.issn 0004-6361
dc.identifier.issn 1432-0746
dc.identifier.other PURE UUID: c303e2bb-92ff-499b-ab4a-72019b6eb97d
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
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dc.description.abstract Period estimation is one of the central topics in astronomical time series analysis, where data is often unevenly sampled. Especially challenging are studies of stellar magnetic cycles, as there the periods looked for are of the order of the same length than the datasets themselves. The datasets often contain trends, the origin of which is either a real long-term cycle or an instrumental effect, but these effects cannot be reliably separated, while they can lead to erroneous period determinations if not properly handled. In this study we aim at developing a method that can handle the trends properly, and by performing extensive set of testing, we show that this is the optimal procedure when contrasted with methods that do not include the trend directly to the model. The effect of the noise model on the results is also investigated. We introduce a Bayesian Generalised Lomb-Scargle Periodogram with Trend (BGLST), which is a probabilistic linear regression model using Gaussian priors for the coefficients and uniform prior for the frequency parameter. We show, using synthetic data, that when there is no prior information on whether and to what extent the true model of the data contains a linear trend, the introduced BGLST method is preferable to the methods which either detrend the data or leave the data untrended before fitting the periodic model. Whether to use different from constant noise model depends on the density of the data sampling as well as on the true noise model of the process. en
dc.format.extent 1-13
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries ASTRONOMY AND ASTROPHYSICS en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Centre of Excellence Research on Solar Long-Term Variability and Effects, ReSoLVE
dc.contributor.department Tartu Observatory
dc.contributor.department Department of Computer Science en
dc.subject.keyword Astrophysics - Solar and Stellar Astrophysics
dc.subject.keyword Astrophysics - Instrumentation and Methods for Astrophysics
dc.subject.keyword Statistics - Applications
dc.subject.keyword Statistics - Machine Learning
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201806183367
dc.identifier.doi 10.1051/0004-6361/201732524
dc.type.version publishedVersion

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