Difference between revisions of "Galaxy morphology auto classification"
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* DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/] | * DESI Legacy Imaging Surveys [https://www.legacysurvey.org/dr9/files/] | ||
==Methods== | ==Methods & Toos== | ||
* Non-Negative Matrix Factorization | * Non-Negative Matrix Factorization | ||
*Image Fourier Power Spectrum | *Image Fourier Power Spectrum | ||
*Watershed (Image Segmentation) | *Watershed (Image Segmentation) | ||
*Morpheus [https://morpheus-project.github.io/morpheus/] | *Morpheus [https://morpheus-project.github.io/morpheus/] | ||
*DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [https://github.com/maxwelltsai/DeepGalaxy] | *DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [https://github.com/maxwelltsai/DeepGalaxy] | ||
| Line 37: | Line 34: | ||
*Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification] | *Unsupervised Image Classification [https://paperswithcode.com/task/unsupervised-image-classification] | ||
*Copulas [https://projecteuclid.org/journals/annals-of-applied-statistics/volume-1/issue-1/Extending-the-rank-likelihood-for-semiparametric-copula-estimation/10.1214/07-AOAS107.full] | *Copulas [https://projecteuclid.org/journals/annals-of-applied-statistics/volume-1/issue-1/Extending-the-rank-likelihood-for-semiparametric-copula-estimation/10.1214/07-AOAS107.full] | ||
==references== | ==references== | ||
#Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [https://academic.oup.com/mnras/article/475/1/894/4725057] | #Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [https://academic.oup.com/mnras/article/475/1/894/4725057] | ||
Revision as of 06:53, 17 February 2022
- This page makes collections for galaxy morphology auto classification project of CSST image survey
projects
started
- merger identification
- Pixel-based deep learning technic (Renhao Ye)
- Deep learning on galaxy morphology profile (QuanFeng Xu)
- Mock galaxy images for CSST (Zhu Chen)
possibilities
- Classification in parameter space (e.g. parameters from Sextractor)
- Special objects from auto-classification
Minutes
- Mar. 4/2021
- Minutes of exercises on Galaxy Classification Meeting [1] (google docs)
datasets
- galaxy pair catalog [2]
- deep learning on galaxy images: training dataset
- galaxy zoo data [3]
- Galaxy Zoo DECaLS[4]
- DESI Legacy Imaging Surveys [5]
Methods & Toos
- Non-Negative Matrix Factorization
- Image Fourier Power Spectrum
- Watershed (Image Segmentation)
- Morpheus [6]
- DeepGalaxy (Deep learning to classify the properties of galaxy mergers) [7]
- GalaxyMorphology [8]
- lenstronomy [9]
- Unsupervised Image Classification [10]
- Copulas [11]
references
- Deep learning for galaxy surface brightness profile fitting, MNRAS, Volume 475, Issue 1, March 2018 [12]
- The weirdest SDSS galaxies: results from an outlier detection algorithm, 2017, MNRAS,465,4530B, [13]
- An automatic taxonomy of galaxy morphology using unsupervised machine learning, 2018, MNRAS, 473, 1108, [14]
- Galaxy morphology classification with deep convolutional neural networks, 2019, Astrophysics and Space Science, Volume 364, Issue 4, article id. 55, [15]
- Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study, 2020, Astronomy and Computing, Volume 30, article id. 100334, [16]
- Shadows in the Dark: Low-surface-brightness Galaxies Discovered in the Dark Energy Survey,2021,ApJS,252,18T,[17]
- Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band image, arXiv:2102.12776,[18]
links
- THE COSMOSTATISTICS INITIATIVE [19]