Deep learning classification in asteroseismology : J/MNRAS/469/4578

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Authors : Hon M. orcid , Stello D., Yu J. (hide) , Stello D., Yu J. et..al

VizieR DOI : 10.26093/cds/vizier.74694578
Bibcode : 2017MNRAS.469.4578H (ADS) (Simbad) (Objects)
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CDS Keywords : Asteroseismology; Stars, giant; Models
UAT : Asteroseismology, Giant stars, Astronomical models

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Inserted into VizieR : 02-Jun-2020
Last modification : 17-Aug-2024

Deep learning classification in asteroseismology. (2017)

Keywords : asteroseismology - methods data analysis - techniques: image processing - stars: oscillations - stars: statistics

Abstract:In the power spectra of oscillating red giants, there are visually distinct features defining stars ascending the red giant branch from those that have commenced helium core burning. We train a 1D convolutional neural network by supervised learning to automatically learn these visual features from images of folded oscillation spectra. By training and testing on Kepler red giants, we achieve an accuracy of up to 99 per cent in separating helium-burning red giants from those ascending the red giant branch. The convolutional neural network additionally shows capability in accurately predicting the evolutionary states of 5379 previously unclassified Kepler red giants, by which we now have greatly increased the number of classified stars. ...(more)
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We obtain the evolutionary state classifications of 5673 Kepler stars based on automated asymptotic period spacing measurements by Vrard et al. (2016A&A...588A..87V, Cat. J/A+A/588/A87, hereafter V16), and add 335 stars from the classification by Mosser et al. (2014A&A...572L...5M, Cat. J/A+A/572/L5, hereafter M14) that are not already in V16's sample, to a total of 6008 stars. We then assign RGB stars with the binary class 0 and HeB stars with class 1. About 30 per cent of stars in the data set are RGB stars. We randomly choose 1008 stars as test data, with the remaining 5000 stars for training. Additionally, we have an unclassified set comprising 8794 Kepler red giants that are known to oscillate but have not been given classifications by V16 or M14. We want to predict the population labels of all stars in our unclassified set using our trained neural network

  • V/133 : Kepler Input Catalog (Kepler Mission Team, 2009)

                


                

                    

                


                

                    

                


                

                    
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