Deep learning on galaxy images: training dataset

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Shen（讨论 | 贡献）2021年11月11日 (四) 08:41的版本 （→‎Mock galaxies）
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This page is used to collect the training dataset for deep learning applications on galaxy images.

If you want to contribute, please contact Shiyin Shen (ssy@shao.ac.cn)

local galaxy sample

  • galaxy zoo data [1]
  • Galaxy Zoo DECaLS[2]
  • stamps of SDSS main sample galaxies(r<17.77) in DESI [3]

high z galaxy sample

  • COSMOS F814W galaxy postages
  • CANDELS 5 fields galaxy postages (H<24.5)

Mock galaxies

  • generate using GalSim (with CSST-like survey parameters)
  • tel_diam=200mm, exptime=150s, lam=5500A, sky_mag=23, psf_sigma=0.2, gain=1, readnoise=5, pixel_scale=0.074
  • generate using GalSim-Hub[4]
  • A sample of CGS galaxies[5] manually move to high redshifts and observed in other surveys(PS1, SDSS stripe82, CANDELS_AEGIS , Hubble Frontier Fields
  • The Carnegie-Irvine Galaxy Survey (CGS) is a long-term program to investigate the photometric and spectroscopic properties of a statistically complete sample of 605 bright (BT < 12.9mag), southern (δ < 0°) galaxies using the facilities at Las Campanas Observatory.
  • contact Shiyin Shen for more infomation
取自“http://cluster.shao.ac.cn/wiki/index.php?title=Deep_learning_on_galaxy_images:_training_dataset&oldid=4318”