Data mining techniques on astronomical spectra data – II. Classification analysis. Issue 4 (12th November 2022)

• Record Type: Journal Article
• Authors: Yang, Haifeng; Zhou, Lichan; Cai, Jianghui; Shi, Chenhui; Yang, Yuqing; Zhao, Xujun; Duan, Juncheng; Yin, Xiaona
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 518:Issue 4(2023)
• Page Start: 5904
• Page End: 5928
• Publication Date: 2022-11-12
• Subjects: methods: data analysis -- techniques: spectroscopic -- software: data analysis; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stac3292
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical data processing. However, it is difficult to select an appropriate classification method in practical scenarios due to the different algorithmic ideas and data characteristics. Here, we present the second work in the data mining series – a review of spectral classification techniques. This work also consists of three parts: a systematic overview of current literature, experimental analyses of commonly used classification algorithms, and source codes used in this paper. First, we carefully investigate the current classification methods in astronomical literature and organize these methods into ten types based on their algorithmic ideas. For each type of algorithm, the analysis is organized from the following three perspectives. (1) their current applications and usage frequencies in spectral classification are summarized; (2) their basic ideas are introduced and preliminarily analysed; (3) the advantages and caveats of each type of algorithm are discussed. Secondly, the classification performance of different algorithms on the unified data sets is analysed. Experimental data are selected from the LAMOST survey and SDSS survey. Six groups of spectral data sets are designed from data characteristics, data qualities, and data volumes to examine the performance of these algorithms. Then the scores of nine basic algorithms are shown and discussed in the experimental analysis. Finally, nine basic algorithms source codes written in python and manuals for usage and improvement are provided.