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Authordc.contributor.authorDonoso Oliva, C.
Authordc.contributor.authorCabrera Vives, G.
Authordc.contributor.authorProtopapas, P.
Authordc.contributor.authorCarrasco, Davis
Authordc.contributor.authorEstévez, P. A.
Admission datedc.date.accessioned2022-01-07T18:06:18Z
Available datedc.date.available2022-01-07T18:06:18Z
Publication datedc.date.issued2021
Cita de ítemdc.identifier.citationMNRAS 000, 1–17 (2015) Preprint 8 June 2021es_ES
Identifierdc.identifier.other10.1093/mnras/stab1598
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/183511
Abstractdc.description.abstractIn the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of lightcurves. Recurrent neural networks (RNNs) are one of the models used for these applications, and the LSTM unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of lightcurves. A traditional technique to address irregular sequences consists of adding the sampling time to the network’s input, but this is not guaranteed to capture sampling irregularities during training. Alternatively, the Phased LSTM unit has been created to address this problem by updating its state using the sampling times explicitly. In this work, we study the effectiveness of the LSTM and Phased LSTM based architectures for the classification of astronomical lightcurves. We use seven catalogs containing periodic and nonperiodic astronomical objects. Our findings show that LSTM outperformed PLSTM on 6/7 datasets. However, the combination of both units enhances the results in all datasets.es_ES
Patrocinadordc.description.sponsorshipANID Millennium Science Initiative ICN12 009 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1171678 11191130 NLHPC ECM-02es_ES
Lenguagedc.language.isoenes_ES
Publisherdc.publisherOxfordes_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
Sourcedc.sourceMonthly Notices of the Royal Astronomical Societyes_ES
Keywordsdc.subjectMethods: data analysises_ES
Keywordsdc.subjectStarses_ES
Keywordsdc.subjectSoftware: developmentes_ES
Keywordsdc.subjectAstronomicales_ES
Keywordsdc.subjectData baseses_ES
Keywordsdc.subjectMethods: statisticales_ES
Títulodc.titleThe effect of phased recurrent units in the classification of multiple catalogues of astronomical light curveses_ES
Document typedc.typeArtículo de revistaes_ES
dc.description.versiondc.description.versionVersión sometida a revisión - Preprintes_ES
dcterms.accessRightsdcterms.accessRightsAcceso abiertoes_ES
Catalogueruchile.catalogadorcrbes_ES
Indexationuchile.indexArtículo de publícación WoSes_ES


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States