“Galaxy morphology auto classification”的版本间差异

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==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]
#The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4530B/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]


==links==
==links==

2021年3月4日 (四) 05:17的版本

  • This page makes collections for galaxy morphology auto classification project of CSST image survey

projects

  • Classification in parameter space (e.g. parameters from Sextractor)
  • Classification on images, CNN like technic
  • Pixel-based deep learning technic
  • Special objects from auto-classification

datasets

  • galaxy zoo data [1]
  • DESI Legacy Imaging Surveys [2]

tools


references

  1. Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [4]
  2. The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [5]
  3. Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[6]

links

  • THE COSMOSTATISTICS INITIATIVE [7]