Galaxy morphological classification using Dynamic Multiscale Attention Network Article Swipe
Brigette Ma
,
Bo Qiu
,
A-Li Luo
,
Qi Li
,
Fuji Ren
,
Mengyao Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/staf1037
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/staf1037
The implementation of large-scale digital sky surveys has collected enormous photometric images, making galaxy morphology classification a current research hotspot. This paper presents a galaxy morphology classification network based on dynamic large convolution kernels and attention feature fusion, named as the Dynamic Multiscale Attention Galaxy Network (DMAGNet). In this study, 15 266 galaxy images from the Galaxy Zoo DECaLS (Dark Energy Camera Legacy Survey) project are used with six categories of galaxy morphologies. The six categories are edge-on, cigar, in-between, round, spiral, and merger. On the test set, the model is demonstrated with superior performance, achieving 97.1 per cent accuracy, 96.8 per cent recall, and a 96.8 per cent F1 score. Furthermore, the proposed network is subjected to extensive evaluation and ablation experiments, and the t-distributed stochastic neighbour embedding (t-SNE) method is used to visualize the morphological features extracted from the test set. The accuracy of Maxvit is 0.969, while the accuracy of DMAGNet reaches 0.971. The experimental results show that this model outperforms known classification models in the galaxy morphology classification task. Finally, this method was applied to 14 174 190 galaxy images from the BASS + MzLS data set of the DESI Legacy Surveys, constructing a galaxy catalogue covering six morphological categories: Edge-On (192 577), In-Between (2353 937), Cigar (299 725), Round (353 358), Spiral (16 067), and Merger (1266 961). Additionally, an Error column was introduced, containing a total of 9691 564 samples, to label images that may be damaged or have quality issues by incorporating information from other catalogues.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/staf1037
- https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/staf1037/63734445/staf1037.pdf
- OA Status
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- References
- 22
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1093/mnras/staf1037Digital Object Identifier
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Galaxy morphological classification using Dynamic Multiscale Attention NetworkWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-07-07Full publication date if available
- Authors
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Brigette Ma, Bo Qiu, A-Li Luo, Qi Li, Fuji Ren, Mengyao LiList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/staf1037Publisher landing page
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https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/staf1037/63734445/staf1037.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/staf1037/63734445/staf1037.pdfDirect OA link when available
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0Total citation count in OpenAlex
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| primary_location.raw_source_name | Monthly Notices of the Royal Astronomical Society |
| primary_location.landing_page_url | https://doi.org/10.1093/mnras/staf1037 |
| publication_date | 2025-07-07 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2227274187, https://openalex.org/W2044738244, https://openalex.org/W2768455873, https://openalex.org/W1987472719, https://openalex.org/W3111030224, https://openalex.org/W2467225246, https://openalex.org/W2515239931, https://openalex.org/W4297302518, https://openalex.org/W4378174676, https://openalex.org/W1924655545, https://openalex.org/W4388098534, https://openalex.org/W4312191768, https://openalex.org/W3096739052, https://openalex.org/W2603384647, https://openalex.org/W2170505850, https://openalex.org/W3203256536, https://openalex.org/W4390058673, https://openalex.org/W4327566316, https://openalex.org/W4392971963, https://openalex.org/W2883005809, https://openalex.org/W4236965008, https://openalex.org/W1678900085 |
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