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SpecZoo: An AI-Powered Platform for Spectral Analysis and Visualization in Science and Education

Yuan-Hao Pu, Guo-Hong Lei, Yang Xu, Xun-Zhou Chen, Hai-Jun Tian

Abstract

Astronomical spectra, which encode rich astrophysical and chemical information, are fundamental to understanding celestial objects and universal laws. The advent of large-scale spectroscopic surveys, generating tens of millions of spectra, presents significant challenges for efficient data processing and analysis. To address these challenges, we develop an AI-powered platform (named ``SpecZoo'') for spectral visualization and analysis. This platform integrates modern information technology and machine learning to lower the barrier to spectral data utilization and enhance research efficiency. Its core functionalities include interactive visualization, automated spectral classification, physical parameter measurement, spectral annotation, and multi-band/multi-modal data fusion, all supported by flexible user and data management systems. It has become an essential tool for the National Astronomical Data Center, directly supporting spectral data processing and research for major projects including LAMOST, SDSS, DESI, and so on. Furthermore, the platform demonstrates strong potential for science-education integration, providing a novel resource for cultivating talent in astronomy and data science.

SpecZoo: An AI-Powered Platform for Spectral Analysis and Visualization in Science and Education

Abstract

Astronomical spectra, which encode rich astrophysical and chemical information, are fundamental to understanding celestial objects and universal laws. The advent of large-scale spectroscopic surveys, generating tens of millions of spectra, presents significant challenges for efficient data processing and analysis. To address these challenges, we develop an AI-powered platform (named ``SpecZoo'') for spectral visualization and analysis. This platform integrates modern information technology and machine learning to lower the barrier to spectral data utilization and enhance research efficiency. Its core functionalities include interactive visualization, automated spectral classification, physical parameter measurement, spectral annotation, and multi-band/multi-modal data fusion, all supported by flexible user and data management systems. It has become an essential tool for the National Astronomical Data Center, directly supporting spectral data processing and research for major projects including LAMOST, SDSS, DESI, and so on. Furthermore, the platform demonstrates strong potential for science-education integration, providing a novel resource for cultivating talent in astronomy and data science.
Paper Structure (16 sections, 11 figures, 2 tables)

This paper contains 16 sections, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Design of SpecZoo
  3. Design Philosophy
  4. Modular Architecture of SpecZoo
  5. Implementation of SpecZoo
  6. Basic Functions
  7. Advanced AI-Powered Features
  8. An Example for Displaying the Main Interface and Validation of the AI Functionalities
  9. Use Cases for Science
  10. Identification of Strong Gravitational Lens Candidates
  11. White Dwarf–Main Sequence Binary Systems
  12. Efficient Identification and Subtype Classification of Carbon Stars
  13. Use Cases for Education
  14. Assist in Foundational Teaching
  15. Support for Research-Oriented Teaching
  16. ...and 1 more sections

Figures (11)

  • Figure S1: SpecZoo design philosophy. The platform integrates AI-powered analysis, educational innovation, and sustainable data management to address the challenges posed by large-scale spectral data.
  • Figure S2: Architectural design of SpecZoo. The platform consists of four layers: User Roles, Visualization and Label, Data Node, and AI, enabling interactive analysis, data management, and AI-powered processing.
  • Figure S3: Specific permissions of characters.
  • Figure S4: Spectral template of a quasar from the Sloan Digital Sky Survey (SDSS).
  • Figure S5: Interface for spectral recognition task labeling(Asterisk indicates required field), enabling users to annotate physical parameters of selected spectral samples.
  • ...and 6 more figures