Intelligent Supernovae Classification Systems in the KDUST context

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.摘要：随着大型天文巡天与多信使天文学的兴起，针对Ia型超新星（Type Ia supernovae）的自动探测与分类任务，已有诸多机器学习技术被用于相关研究。本文针对KDUST项目（KDUST project）的未来光谱仪系统，在基准测试框架下，基于三种不同的方法论提出了三种解决方案。本文所呈现的三类系统分别为：CINTIA（基于分层神经网络架构）、SUZAN（整合了模糊系统（fuzzy systems）方案）与DANI（基于卷积神经网络（Convolutional Neural Networks）的深度学习方法）。本文阐述了各系统的特性，并基于包含15134条光谱的数据集完成了基准测试。在Ia型超新星与其他光谱类型的分类任务中，DANI架构取得了最优性能，分类准确率可达96%。