“Deep learning on galaxy images: training dataset”的版本间差异
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==high z galaxy sample== |
==high z galaxy sample== |
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* COSMOS F814W galaxy postages |
* COSMOS F814W galaxy postages |
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* COSMOS F814W structural parameters (Sersic index, Re, CAS, GINI, M20) catalog (F814W<23.5) |
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* CANDELS 5 fields galaxy postages (H<24.5) |
* CANDELS 5 fields galaxy postages (H<24.5) |
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* CANDELS 5 fields GALFIT structural parameters catalog |
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* CANDELS 5 fields two components structural parameters catalog (H<23, 0<z<2) |
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contact zhuchen@staff.shnu.edu.cn for more information |
contact zhuchen@staff.shnu.edu.cn for more information |
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*generate using GalSim-Hub[https://arxiv.org/abs/2008.03833] |
*generate using GalSim-Hub[https://arxiv.org/abs/2008.03833] |
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* A sample of CGS galaxies[https://arxiv.org/abs/1111.4605] manually move to high redshifts and |
* A sample of CGS galaxies[https://arxiv.org/abs/1111.4605] 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 |
2021年11月11日 (四) 08:58的最新版本
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]
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