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Universe
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New Discoveries in Astronomical Data (II)
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New Discoveries in Astronomical Data (II)

A special issue of Universe (ISSN 2218-1997). This special issue belongs to the section "Astroinformatics and Astrostatistics".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1014

Editors

Prof. Dr. Yanxia Zhang

E-Mail Website
Guest Editor
CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
Interests: astroinformatics; astrostatistics; multi-wavelength astronomy; quasars and active galactic nuclei
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. A-Li Luo

E-Mail Website
Guest Editor
1. CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Interests: astronomical data processing; spectral analysis; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first edition of this Special Issue (New Discoveries in Astronomical Data), we are pleased to launch the second edition to further explore the latest breakthroughs and methodologies in this rapidly evolving field.

The advent of ground- and space-based observatories (e.g., SDSS, LAMOST, ZTF, Pan-STARRS, FAST, WISE, GAIA, JWST, LSST, Euclid) has propelled astronomy firmly into the big data era. The resulting data deluge presents profound challenges in processing, analysis, and interpretation due to its volume, complexity, and heterogeneity. These challenges, in turn, drive the development of innovative methods and offer unprecedented potential for discovery.

This Special Issue aims to collect and disseminate cutting-edge research addressing these opportunities. We invite contributions on novel data processing techniques, feature engineering methods, and analytical frameworks to extract new knowledge from modern astronomical datasets.

We welcome original research and review articles that address, but are not limited to, the following topics:

  • Machine learning and deep learning applications in astronomy;
  • Advanced methods for feature extraction, selection, and classification;
  • Pipelines and analysis for time-domain and multi-messenger astronomy;
  • Discovery and characterization of rare or new celestial objects;
  • Methodological advances for handling high-dimensional and massive datasets.

Prof. Dr. Yanxia Zhang
Prof. Dr. A-Li Luo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Universe is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • discoveries: from radio to gamma-rays
  • astrostatistics and astroinformatics
  • data analysis: methods
  • statistical: astronomical data bases

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 1868 KB  
Article
Self-Supervised Spectral Representation Learning for LAMOST
by Wenjun Zhang, Anhua Zhou, Lei Yuan, Yuchen Liang, Yihan Song and Zhenping Yi
Universe 2026, 12(6), 181; https://doi.org/10.3390/universe12060181 - 17 Jun 2026
Viewed by 243
Abstract
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has collected tens of millions of spectra, providing an unprecedented resource for large-scale spectroscopic studies. Efficient retrieval techniques are therefore essential for exploring such massive datasets. Existing approaches often rely on predefined templates or [...] Read more.
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has collected tens of millions of spectra, providing an unprecedented resource for large-scale spectroscopic studies. Efficient retrieval techniques are therefore essential for exploring such massive datasets. Existing approaches often rely on predefined templates or manually labeled training samples, which can limit their applicability in large and diverse spectral archives. In this work, we present a general similarity-retrieval framework that combines self-supervised contrastive learning based on a convolutional neural network with Facebook AI Similarity Search (FAISS) for efficient large-scale spectral retrieval. The framework learns spectral representations directly from unlabeled data and enables flexible retrieval from user-defined wavelength regions based on feature similarity. We evaluate the framework on several stellar populations in LAMOST DR8. For late-type M8-star retrieval, 90.5% of the top 1000 retrieved spectra are later than M6. For M0–M5 giants, the mean retrieval accuracy across six subtypes reaches 94.8%. Using a C-H star spectrum as the query spectrum, 90.8% of the top 1000 retrieved candidates are classified as carbon stars by the LAMOST pipeline. Cross-matching with SIMBAD further confirms 255 C-H stars and 47 C-R stars among the retrieved candidates. These results demonstrate that the proposed framework can efficiently identify spectrally similar objects across large spectroscopic databases and can serve as a useful tool for searching for rare or spectrally distinctive stellar populations. Full article
(This article belongs to the Special Issue New Discoveries in Astronomical Data (II))
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21 pages, 1355 KB  
Article
GCSNet: A Multi-Modal Fusion Network with Cosine Similarity for Galaxy Classification
by Siyi Zhang, Liangping Tu, Jiawei Miao and Bing Su
Universe 2026, 12(6), 159; https://doi.org/10.3390/universe12060159 - 29 May 2026
Viewed by 209
Abstract
Galaxy classification is essential for understanding the formation and evolution of cosmic structures. However, faced with the explosive growth of astronomical observation data, traditional single-modality classification methods relying solely on spectroscopy or imaging have struggled to meet high-precision demands due to insufficient feature [...] Read more.
Galaxy classification is essential for understanding the formation and evolution of cosmic structures. However, faced with the explosive growth of astronomical observation data, traditional single-modality classification methods relying solely on spectroscopy or imaging have struggled to meet high-precision demands due to insufficient feature utilization and limited generalization capability. Therefore, multimodal fusion has emerged as a promising direction by leveraging information complementarity to overcome the limitations of single data sources. Accordingly, this paper proposes a model named Galaxy CosineNet (GCSNet), which integrates imaging, spectroscopic, and tabular data for high-precision galaxy classification. Specifically, the model employs dedicated encoders to process the three modalities separately and utilizes skip connections to preserve raw features. Furthermore, it incorporates a multi-head self-attention mechanism to deeply mine global cross-modal complementary information. Finally, these features are concatenated and fed into a cosine similarity classification head. Experimental results demonstrate that GCSNet achieves 97.15% accuracy in classifying star-forming, composite, active galactic nuclei (AGNs), and normal galaxies. This performance outperforms the best single-modal baseline, GaSNet, by 0.76% and mainstream multi-modal models such as MB-ISTL and the Transformer by over 1.6%. Consequently, the proposed GCSNet offers an effective and novel approach for research on automatic galaxy classification. Full article
(This article belongs to the Special Issue New Discoveries in Astronomical Data (II))
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