Intelligent Supernovae Classification Systems In The Kdust Context

With the advent of Large astronomical surveys plus multi-messenger astronomy, both automatic detection and classification of Type la 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 la supernovae in relation to other spectral types.