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Machine Learning Approaches for Supernovae Classification
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Author(s): Surbhi Agrawal (PESIT-BSC, India), Kakoli Bora (PESIT-BSC, India)and Swati Routh (Jain University, India)
Copyright: 2017
Pages: 13
Source title:
Handbook of Research on Applied Cybernetics and Systems Science
Source Author(s)/Editor(s): Snehanshu Saha (PESIT South Campus, India), Abhyuday Mandal (University of Georgia, USA), Anand Narasimhamurthy (BITS Hyderabad, India), Sarasvathi V (PESIT- Bangalore South Campus, India)and Shivappa Sangam (UGC, India)
DOI: 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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