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Intelligent Supernovae Classification Systems in the KDUST context.

LuÍs R Arantes Filho1, Reinaldo R Rosa2, Lamartine N F GuimarÃes3

  • 1National Institute for Space Research (INPE), Applied Computing Graduate Program (CAP), Av. dos Astronautas, 1758, Jd. da Granja, 12227-010 São José dos Campos, SP, Brazil.

Anais Da Academia Brasileira De Ciencias
|February 24, 2021
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Summary
This summary is machine-generated.

Machine learning models CINTIA, SUZAN, and DANI were benchmarked for classifying Type Ia supernovae. The Deep Learning Convolutional Neural Network model DANI achieved the highest accuracy at 96%.

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Area of Science:

  • Astronomy and Astrophysics
  • Machine Learning
  • Spectroscopy

Background:

  • Large astronomical surveys and multi-messenger astronomy necessitate efficient methods for supernova detection and classification.
  • Machine learning techniques are increasingly applied to astronomical data analysis.

Purpose of the Study:

  • To benchmark three distinct machine learning methodologies for the automatic classification of Type Ia supernovae.
  • To evaluate these systems for the KDUST (Kilo-Degree Ultraviolet Survey Telescope) future spectrometer.

Main Methods:

  • Development and comparison of three systems: CINTIA (hierarchical neural networks), SUZAN (fuzzy systems), and DANI (Deep Learning with Convolutional Neural Networks).
  • A dataset of 15,134 spectra was used for performance evaluation.

Main Results:

  • The DANI architecture, utilizing Deep Learning with Convolutional Neural Networks, demonstrated superior performance.
  • DANI achieved 96% accuracy in classifying Type Ia supernovae against other spectral types.

Conclusions:

  • Deep Learning with Convolutional Neural Networks (DANI) is the most effective method among the tested approaches for Type Ia supernova classification.
  • The developed systems show promise for future spectroscopic surveys like KDUST.