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- W2891641180 abstract "Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep Variational Autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion." @default.
- W2891641180 created "2018-09-27" @default.
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- W2891641180 date "2018-12-01" @default.
- W2891641180 modified "2023-10-17" @default.
- W2891641180 title "Stellar Cluster Detection Using GMM with Deep Variational Autoencoder" @default.
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- W2891641180 doi "https://doi.org/10.1109/raics.2018.8634903" @default.
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