Unsupervised learning of structure in spectroscopic cubes
Astronomy and Computing, pp. 25-35, 2018.
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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