Difference between revisions of "Galaxy morphology auto classification"
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*GalaxyMorphology [https://github.com/alexhock/galaxymorphology#galaxy-morphology] | *GalaxyMorphology [https://github.com/alexhock/galaxymorphology#galaxy-morphology] | ||
*lenstronomy [https://github.com/sibirrer/lenstronomy] | *lenstronomy [https://github.com/sibirrer/lenstronomy] | ||
*Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification] | |||
==references== | ==references== |
Revision as of 08:57, 22 April 2021
- This page makes collections for galaxy morphology auto classification project of CSST image survey
projects
started
- merger identification
- Morphology of Alfalfa galaxies
- Unsupervised classification on images
- bar features
possibilities
- Classification in parameter space (e.g. parameters from Sextractor)
- Pixel-based deep learning technic
- Special objects from auto-classification
Meetings
- Mar. 4/2021
- Minutes of exercises on Galaxy Classification Meeting [1]
datasets
tools
- Morpheus [4]
- DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [5]
- GalaxyMorphology [6]
- lenstronomy [7]
- Unsupervised Image Classification [8]
references
- Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [9]
- The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017,MNRAS,465,4530B, [10]
- An automatic taxonomy of galaxy morphology using unsupervised machine learning, 2018, MNRAS, 473, 1108, [11]
- Galaxy morphology classification with deep convolutional neural networks, 2019, Astrophysics and Space Science, Volume 364, Issue 4, article id. 55, [12]
- Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study, 2020, Astronomy and Computing, Volume 30, article id. 100334, [13]
- Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[14]
- Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[15]
links
- THE COSMOSTATISTICS INITIATIVE [16]