Application of machine learning in astronomical spectral data mining  

作　　者：Ting Zhang Hailong Zhang Yazhou Zhang Xu Du Wenna Cai Han Wu Yuyue Jiao Wanqiong Wang Jie Wang Xinchen Ye Jia Li 

机构地区：[1]Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Key Laboratory of Radio Astronomy,Chinese Academy of Sciences,Nanjing 210008,China [4]National Astronomical Data Center,Beijing 100101,China

出　　处：《Astronomical Techniques and Instruments》2025年第2期73-87,共15页天文技术与仪器(英文)

基　　金：supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502);the National Natural Science Foundation of China(NSFC)(12173077);the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112);the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01);China National Astronomical Data Center(NADC);the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences;Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360).

摘　　要：Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectroscopy faces the dual challenges of rapidly expanding data volumes and relatively lagging data processing capabilities.In this context,the rise of artificial intelligence technologies offers an innovative solution to address these challenges.This paper analyzes the latest developments in the application of machine learning for astronomical spectral data mining and discusses future research directions in AI-based spectral studies.However,the application of machine learning technologies presents several challenges.The high complexity of models often comes with insufficient interpretability,complicating scientific understanding.Moreover,the large-scale computational demands place higher requirements on hardware resources,leading to a significant increase in computational costs.AI-based astronomical spectroscopy research should advance in the following key directions.First,develop efficient data augmentation techniques to enhance model generalization capabilities.Second,explore more interpretable model designs to ensure the reliability and transparency of scientific conclusions.Third,optimize computational efficiency and reduce the threshold for deep-learning applications through collaborative innovations in algorithms and hardware.Furthermore,promoting the integration of cross-band data processing is essential to achieve seamless integration and comprehensive analysis of multi-source data,providing richer,multidimensional information to uncover the mysteries of the universe.

关 键 词：Machine learning Neural networks Stellar atmospheric parameter prediction Stellar spectral classification 

分 类 号：P111[天文地球—天文学]