“Galaxy morphology auto classification”的版本间差异

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*lenstronomy [https://github.com/sibirrer/lenstronomy]
*lenstronomy [https://github.com/sibirrer/lenstronomy]
*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]

==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]

2021年8月29日 (日) 10:36的版本

  • 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)
  • Pixel-based deep learning technic
  • Special objects from auto-classification

Meetings

datasets

  • galaxy zoo data [2]
  • Galaxy Zoo DECaLS[3]
  • DESI Legacy Imaging Surveys [4]
  • galaxy pair catalog [5]

Methods

  • Non-Negative Matrix Factorization
  • Image Fourier Power Spectrum
  • Auto-encoders (Rupesh)
  • Watershed (Image Segmentation)

tools

  • 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

  • THE COSMOSTATISTICS INITIATIVE [19]