“Galaxy morphology auto classification”的版本间差异

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===started===
===started===
* [[merger identification]]
* [[merger identification]]
* [[Pixel-based deep learning technic]] (Renhao Ye)
* [[pixel based galaxy morphology classification]] (Renhao Ye)
* [[Deep learning on galaxy morphology profile]] (QuanFeng Xu)
* [[Deep learning on galaxy morphology profile]] (QuanFeng Xu)
* [[Mock galaxy images for CSST]] (Zhu Chen)
* [[Mock galaxy images for CSST]] (Zhu Chen)
* [[Auto clustering of galaxies after dimensionality reduction]] （Quanfeng Xu/Rupesh）


===possibilities===
===possibilities===
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* DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/]
* DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/]


==Methods==
==Methods & Toos==
* Non-Negative Matrix Factorization
* Non-Negative Matrix Factorization
*Image Fourier Power Spectrum
*Image Fourier Power Spectrum
*Auto-encoders (Rupesh)
*Watershed (Image Segmentation)
*Watershed (Image Segmentation)

==tools==
*Morpheus [https://morpheus-project.github.io/morpheus/]
*Morpheus [https://morpheus-project.github.io/morpheus/]
*DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [https://github.com/maxwelltsai/DeepGalaxy]
*DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [https://github.com/maxwelltsai/DeepGalaxy]
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*Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification]
*Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification]
*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]
*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]

==references==
==references==
#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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==links==
==links==
* GPU server in SHAO [http://cluster.shao.ac.cn/wiki/index.php?title=Internal:Nserver_Service]
* GPU server in ShNU
*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

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 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

  1. Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [12]
  2. The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017, MNRAS,465,4530B, [13]
  3. An automatic taxonomy of galaxy morphology using unsupervised machine learning, 2018, MNRAS, 473, 1108, [14]
  4. Galaxy morphology classification with deep convolutional neural networks, 2019, Astrophysics and Space Science, Volume 364, Issue 4, article id. 55, [15]
  5. Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study, 2020, Astronomy and Computing, Volume 30, article id. 100334, [16]
  6. Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[17]
  7. Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[18]

links

  • GPU server in SHAO [19]
  • GPU server in ShNU
  • THE COSMOSTATISTICS INITIATIVE [20]
取自“http://cluster.shao.ac.cn/wiki/index.php?title=Galaxy_morphology_auto_classification&oldid=5027”