“Galaxy morphology auto classification”的版本间差异
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无编辑摘要 |
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(未显示3个用户的34个中间版本) | |||
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==projects== |
==projects== |
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===started=== |
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* Classification on images, CNN like technic |
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* [[merger identification]] |
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:*[[Morphology of Alfalfa galaxies]] |
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* [[pixel based galaxy morphology classification]] (Renhao Ye) |
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* Pixel-based deep learning technic |
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* [[Deep learning on galaxy morphology profile]] (QuanFeng Xu) |
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* [[Mock galaxy images for CSST]] (Zhu Chen) |
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* [[Auto clustering of galaxies after dimensionality reduction]] (Quanfeng Xu/Rupesh) |
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===possibilities=== |
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* Special objects from auto-classification |
* Special objects from auto-classification |
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== |
==Minutes== |
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* [[Mar. 4/2021 |
* [[1st_seminor_morphology | Mar. 4/2021 ]] |
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* Minutes of exercises on Galaxy Classification Meeting [https://docs.google.com/document/d/1s2qEoL9QufVWu2Cp92cEx-F1I04Kw7FA_tycE9kkYIM/edit?usp=sharing] (google docs) |
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* Apr. 1/2021 |
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==datasets== |
==datasets== |
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*galaxy |
* galaxy pair catalog [http://202.127.29.3/~shen/isopair/] |
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* [[deep learning on galaxy images: training dataset]] |
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* galaxy zoo data [https://data.galaxyzoo.org/] |
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:*Galaxy Zoo DECaLS[https://zenodo.org/record/4196267#.YIE8TB1LhOQ] |
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* DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/] |
* DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/] |
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==Methods & Toos== |
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* Non-Negative Matrix Factorization |
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*Image Fourier Power Spectrum |
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*Watershed (Image Segmentation) |
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*Morpheus [https://morpheus-project.github.io/morpheus/] |
*Morpheus [https://morpheus-project.github.io/morpheus/] |
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*DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [https://github.com/maxwelltsai/DeepGalaxy] |
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*GalaxyMorphology [https://github.com/alexhock/galaxymorphology#galaxy-morphology] |
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*lenstronomy [https://github.com/sibirrer/lenstronomy] |
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*Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification] |
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*Copulas [https://projecteuclid.org/journals/annals-of-applied-statistics/volume-1/issue-1/Extending-the-rank-likelihood-for-semiparametric-copula-estimation/10.1214/07-AOAS107.full] |
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==references== |
==references== |
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#Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [https://academic.oup.com/mnras/article/475/1/894/4725057] |
#Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [https://academic.oup.com/mnras/article/475/1/894/4725057] |
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#The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4530B/abstract] |
#The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017, MNRAS,465,4530B, [https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4530B/abstract] |
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#An automatic taxonomy of galaxy morphology using unsupervised machine learning, 2018, MNRAS, 473, 1108, [https://arxiv.org/abs/1709.05834] |
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#Galaxy morphology classification with deep convolutional neural networks, 2019, Astrophysics and Space Science, Volume 364, Issue 4, article id. 55, [https://arxiv.org/abs/1807.10406] |
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#Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study, 2020, Astronomy and Computing, Volume 30, article id. 100334, [https://arxiv.org/abs/1901.07047] |
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#Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[https://ui.adsabs.harvard.edu/abs/2021ApJS..252...18T/abstract] |
#Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[https://ui.adsabs.harvard.edu/abs/2021ApJS..252...18T/abstract] |
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#Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[https://arxiv.org/abs/2102.12776] |
#Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[https://arxiv.org/abs/2102.12776] |
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==links== |
==links== |
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* GPU server in SHAO [http://cluster.shao.ac.cn/wiki/index.php?title=Internal:Nserver_Service] |
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* GPU server in ShNU |
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*THE COSMOSTATISTICS INITIATIVE [https://cosmostatistics-initiative.org/] |
*THE COSMOSTATISTICS INITIATIVE [https://cosmostatistics-initiative.org/] |
2022年10月1日 (六) 09:19的最新版本
- 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 (Quanfeng Xu/Rupesh)
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]