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Pushing the Technical Frontier: From Overwhelmingly Large Data Sets to Machine Learning

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arxiv 1901.05978 v1 pith:WU54GDCZ submitted 2019-01-17 astro-ph.IM

Viviana Acquaviva This is my paper · ORCID
classification astro-ph.IM
keywords datagalaxylargelearningmachineassociatedchallengescurrent
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This paper summarizes my thoughts, given in an invited review at the IAU symposium 341 "Challenges in Panchromatic Galaxy Modelling with Next Generation Facilities", about how machine learning methods can help us solve some of the big data problems associated with current and upcoming large galaxy surveys.

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  1. Machine Learning Classifiers for Intermediate Redshift Emission Line Galaxies

    astro-ph.GA 2019-08 conditional novelty 6.0 of 10

    A random forest trained on BPT-classified SDSS galaxies can classify z=0.3-0.8 emission line galaxies into four subtypes using only optical features, with the best performance among four machine learning methods tested.

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