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    Area of Science:

    • Astronomy and Astrophysics
    • Computational Astrophysics
    • Data Science

    Background:

    • Celestial spectra provide crucial astrophysical information about celestial bodies.
    • Massive spectral data generation from surveys like LAMOST necessitates efficient processing technologies.
    • Automatic classification of stellar spectra is fundamental for spectral data analysis.

    Purpose of the Study:

    • To research and develop an automatic classification method for massive stellar spectra.
    • To classify stellar spectra into F, G, and K subtypes using Lick line indices.
    • To enhance the efficiency of processing and analyzing large spectral datasets.

    Main Methods:

    • Calculating Lick line indices as characteristic vectors for stellar spectra.
    • Employing a Bayesian decision method for spectral classification.
    • Implementing parallel computation of Lick indices and Bayesian classification on the Hadoop MapReduce framework for massive datasets.

    Main Results:

    • Successfully classified stellar spectra into F, G, and K subtypes using Lick indices and Bayesian methods.
    • Significantly improved the efficiency of analyzing and processing massive spectral data through parallel computation on Hadoop.
    • Demonstrated the effectiveness of the Hadoop MapReduce framework for high-throughput spectral analysis.

    Conclusions:

    • The Lick index combined with the Bayesian decision method provides an effective approach for stellar spectral classification.
    • The Hadoop MapReduce framework offers a scalable and efficient solution for processing and classifying massive astronomical spectral data.
    • This research contributes to advancing automated analysis techniques in astrophysics, enabling better utilization of large-scale survey data.