Deep learning on galaxy images: training dataset

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
• COSMOS F814W structural parameters (Sersic index, Re, CAS, GINI, M20) catalog (F814W<23.5)
• CANDELS 5 fields galaxy postages (H<24.5)
• CANDELS 5 fields GALFIT structural parameters catalog
• CANDELS 5 fields two components structural parameters catalog (H<23, 0<z<2)

contact zhuchen@staff.shnu.edu.cn for more information

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 mocked in other surveys(PS1, SDSS stripe82, CANDELS_AEGIS , Hubble Frontier Fields) , contact ssy@shao.ac.cn for more information