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Characterizing Hα emitters from the S-PLUS survey, combining photometric data from GALEX, GAIA and SDSS

Authors: Karagiannis Anastasios; Akras Stavros;

Characterizing Hα emitters from the S-PLUS survey, combining photometric data from GALEX, GAIA and SDSS

Abstract

Data from the SPLUS survey have been cross-matched with GALEX, SDSS, GAIA, 2MASS and WISE with the aim to characterize Hα sources. The study was focused on Hα emitters and their link with UV emitters. Machine learning algorithms were also employed for the separation of Hα emitters in S-PLUS survey of UV and non-UV sources based on GALEX.

Keywords

machine learning, prediction models, Hα emmiters, diagnosstic diagrams, photometric data, random forest, classification tree

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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