“Galaxy morphology auto classification”的版本间差异
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#The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4530B/abstract] |
#The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.4530B/abstract] |
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#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] |
#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] |
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#Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[https://arxiv.org/abs/2102.12776] |
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2021年3月4日 (四) 05:41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
tools
- Morpheus [3]
references
- Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [4]
- The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [5]
- Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[6]
- Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[7]
links
- THE COSMOSTATISTICS INITIATIVE [8]