The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Machine Learning Approaches for Supernovae Classification
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).
Related Content
Jaime Salvador, Zoila Ruiz, Jose Garcia-Rodriguez.
© 2020.
12 pages.
|
Stavros Pitoglou.
© 2020.
11 pages.
|
Mette L. Baran.
© 2020.
13 pages.
|
Yingxu Wang, Victor Raskin, Julia M. Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, Newton Howard.
© 2020.
15 pages.
|
Yingxu Wang, Lotfi A. Zadeh, Bernard Widrow, Newton Howard, Françoise Beaufays, George Baciu, D. Frank Hsu, Guiming Luo, Fumio Mizoguchi, Shushma Patel, Victor Raskin, Shusaku Tsumoto, Wei Wei, Du Zhang.
© 2020.
18 pages.
|
Nayem Rahman.
© 2020.
24 pages.
|
Amir Manzoor.
© 2020.
27 pages.
|
|
|