Unsupervised learning of structure in spectroscopic cubes

Astronomy and Computing, pp. 25-35, 2018.

Cited by: 0|Bibtex|Views80|DOI:https://doi.org/10.1016/j.ascom.2018.06.001
Other Links: academic.microsoft.com|arxiv.org|www.sciencedirect.com

Abstract:

We consider the problem of analyzing the structure of spectroscopic cubes using unsupervised machine learning techniques. We propose representing the target’s signal as a homogeneous set of volumes through an iterative algorithm that separates the structured emission from the background while not overestimating the flux. Besides verifying...More

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