An Astronomical Image Content-based Recommendation System Using Combined Deep Learning Models in a Fully Unsupervised Mode

@article{Teimoorinia2021AnAI,
  title={An Astronomical Image Content-based Recommendation System Using Combined Deep Learning Models in a Fully Unsupervised Mode},
  author={Hossen Teimoorinia and Sara Shishehchi and Ahnaf Tazwar and Ping-Cherng Lin and Finn Archinuk and Stephen D. J. Gwyn and JJ. Kavelaars},
  journal={The Astronomical Journal},
  year={2021},
  volume={161}
}
We have developed a method that maps large astronomical images onto a two-dimensional map and clusters them. A combination of various state-of-the-art machine-learning algorithms is used to develop a fully unsupervised image-quality assessment and clustering system. Our pipeline consists of a data pre-processing step where individual image objects are identified in a large astronomical image and converted to smaller pixel images. This data is then fed to a deep convolutional auto-encoder… 

Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning

Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of diverse spectra covering different regions of thousands of galaxies. We propose and implement a deep unsupervised

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