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Machine Learning Approaches for Supernovae Classification

Machine Learning Approaches for Supernovae Classification

Surbhi Agrawal, Kakoli Bora, Swati Routh
Copyright: © 2017 |Pages: 13
ISBN13: 9781522524984|ISBN10: 1522524983|EISBN13: 9781522524991
DOI: 10.4018/978-1-5225-2498-4.ch009
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MLA

Agrawal, Surbhi, et al. "Machine Learning Approaches for Supernovae Classification." Handbook of Research on Applied Cybernetics and Systems Science, edited by Snehanshu Saha, et al., IGI Global, 2017, pp. 207-219. https://doi.org/10.4018/978-1-5225-2498-4.ch009

APA

Agrawal, S., Bora, K., & Routh, S. (2017). Machine Learning Approaches for Supernovae Classification. In S. Saha, A. Mandal, A. Narasimhamurthy, S. V, & S. Sangam (Eds.), Handbook of Research on Applied Cybernetics and Systems Science (pp. 207-219). IGI Global. https://doi.org/10.4018/978-1-5225-2498-4.ch009

Chicago

Agrawal, Surbhi, Kakoli Bora, and Swati Routh. "Machine Learning Approaches for Supernovae Classification." In Handbook of Research on Applied Cybernetics and Systems Science, edited by Snehanshu Saha, et al., 207-219. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2498-4.ch009

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Abstract

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

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