Intelligent Supernovae Classification Systems in the KDUST context
| Autor: | LUÍS R. ARANTES FILHO, REINALDO R. ROSA, LAMARTINE N.F. GUIMARÃES |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Anais da Academia Brasileira de Ciências, Vol 93, Iss suppl 1 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1678-2690 0001-3765 |
| DOI: | 10.1590/0001-3765202120200862 |
| Popis: | Abstract With the advent of large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type Ia supernovae have been addressed by different machine learning techniques. In this article we present three solutions aimed at the future spectrometer of the KDUST project, within a scope of benchmark, considering three different methodologies. The systems presented here are the following: CINTIA (based on hierarchical neural network architecture), SUZAN (which incorporates the solution known as fuzzy systems) and DANI (based on Deep Learning with Convolutional Neural Networks). The characteristics of the systems are presented and the benchmark is performed considering a data set containing 15.134 spectra. The best performance is obtained by the DANI architecture which provides 96% accuracy in the classification of Type Ia supernovae in relation to other spectral types. |
| Databáze: | Directory of Open Access Journals |
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