Abstract
Three methods for using machine learning to decide if a star has an exoplanet from transit survey data are discussed in this paper and are also used to determine which approach performs better on a labeled data set containing light intensity time series from extrasolar stars. Convolutional Neural Network (C.N.N.), a C.N.N. autoencoder, and Support Vector Machines ( S.V.M.) are among the three models. We trained the models using data from the Kepler Space Telescope; because there were very few confirmed exoplanet in the data, some preprocessing is performed before implementing the data set methods. SMOTE (Synthetic Minority Oversampling Technique) was used to over-sample the exoplanet class to further boost the performance. For the supervised learning approaches, CNN and SVM, the outcomes were promising. Therefore, CNN autoencoder, the unsupervised method, did not generate meaningful results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Research Publication and Reviews
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
