“Galaxy morphology auto classification”的版本间差异
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无编辑摘要 |
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* [[Deep learning on galaxy morphology profile]] (QuanFeng Xu) |
* [[Deep learning on galaxy morphology profile]] (QuanFeng Xu) |
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* [[Mock galaxy images for CSST]] (Zhu Chen) |
* [[Mock galaxy images for CSST]] (Zhu Chen) |
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* [[Auto clustering of galaxies after dimensionality reduction]] （Rupesh/Quanfeng Xu） |
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===possibilities=== |
===possibilities=== |
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2022年3月4日 (五) 15:27的版本
- This page makes collections for galaxy morphology auto classification project of CSST image survey
projects
started
- merger identification
- pixel based galaxy morphology classification (Renhao Ye)
- Deep learning on galaxy morphology profile (QuanFeng Xu)
- Mock galaxy images for CSST (Zhu Chen)
- Auto clustering of galaxies after dimensionality reduction （Rupesh/Quanfeng Xu）
possibilities
- Classification in parameter space (e.g. parameters from Sextractor)
- Special objects from auto-classification
Minutes
- Mar. 4/2021
- Minutes of exercises on Galaxy Classification Meeting [1] (google docs)
datasets
- galaxy pair catalog [2]
- deep learning on galaxy images: training dataset
- galaxy zoo data [3]
- Galaxy Zoo DECaLS[4]
- DESI Legacy Imaging Surveys [5]
Methods & Toos
- Non-Negative Matrix Factorization
- Image Fourier Power Spectrum
- Watershed (Image Segmentation)
- Morpheus [6]
- DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [7]
- GalaxyMorphology [8]
- lenstronomy [9]
- Unsupervised Image Classification [10]
- Copulas [11]
references
- Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [12]
- The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017, MNRAS,465,4530B, [13]
- An automatic taxonomy of galaxy morphology using unsupervised machine learning, 2018, MNRAS, 473, 1108, [14]
- Galaxy morphology classification with deep convolutional neural networks, 2019, Astrophysics and Space Science, Volume 364, Issue 4, article id. 55, [15]
- Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study, 2020, Astronomy and Computing, Volume 30, article id. 100334, [16]
- Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[17]
- Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[18]