Identifying Variable Stars from Kepler Data Using Machine Learning

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dc.contributor.authorAdassuriya, J.
dc.contributor.authorJayasinghe, J. A. N. S. S.
dc.contributor.authorJayaratne, K. P. S. C.
dc.date.accessioned2021-08-17T14:24:11Z
dc.date.available2021-08-17T14:24:11Z
dc.date.issued2021-07-30
dc.description.abstractMachine learning algorithms play an impressive role in modern technology and address automation problems in many fields as these techniques can be used to identify features with high sensitivity, which humans or other programming techniques aren’t capable of detecting. In addition, the growth of the availability of the data demands the need of faster, accurate, and more reliable automating methods of extracting information, reforming, and preprocessing, and analyzing them in the world of science. The development of machine learning techniques to automate complex manual programs is a time relevant research in astrophysics as it’s a field where, experts are dealing with large sets of data every day. In this study, an automated classification was built for 6 types of star classes Beta Cephei, Delta Scuti, Gamma Doradus, Red Giants, RR Lyrae and RV Tarui with widely varying properties, features extracted from training dataset of stellar light curves obtained from Kepler mission. The Random Forest classification model was used as the Machine Learning model and both periodic and non-periodic features extracted from light curves were used as the inputs to the model. Our implementation achieved an accuracy of 86.5%, an average precision level of 0.86, an average recall value of 0.87, and average F1-Score of 0.86 for the testing dataset obtained from the Kepler mission.en_US
dc.description.sponsorshipThis paper includes data collected by the Keplermission. Funding for the Kepler mission is provided by the NASA Science Mission directorate.en_US
dc.description.urihttps://ej-physics.org/index.php/ejphysics/article/view/93en_US
dc.format.extent6 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2wlrs-jivq
dc.identifier.citationAdassuriya, J.; Jayasinghe, J. A. N. S. S.; Jayaratne, K. P. S. C.; Identifying Variable Stars from Kepler Data Using Machine Learning; European Journal of Applied Physics, 3,4, 30 July, 2021; https://doi.org/10.24018/ejphysics.2021.3.4.93en_US
dc.identifier.urihttps://doi.org/10.24018/ejphysics.2021.3.4.93
dc.identifier.urihttp://hdl.handle.net/11603/22481
dc.language.isoen_USen_US
dc.publisherEuropean Journal of Applied Physicsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.titleIdentifying Variable Stars from Kepler Data Using Machine Learningen_US
dc.typeTexten_US

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