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Title Galactic activity diagnostics based on WISE photometry and machine learning methods
Author Δαούτης, Χαράλαμπος
Thesis advisor Ζέζας, Ανδρέας
Abstract One of the most important and difficult problems in modern Astrophysics is the classification of galaxies based on their activity. A lot of progress has been done over the years with numerous diagnostics to have been developed; optical and infrared methods being the most successful and popular among them. In the recent years with the advent of the allsky surveys from space telescopes, infrared diagnostics for AGN selection methods have become popular. Unfortunately, we find that some of them are neither complete or reliable in galaxies located in the local Universe. In addition, the class of passive galaxies is absent from these diagnostics. For these reasons, we embarked in the development of a new threedimensional activity diagnostic based on machine-learning methods and WISE infrared photometry. In this project, we consider the classes of star-forming, AGN, composite and passive galaxies. We find that a diagnostic based on three features derived from the three WISE bands (1, 2 and 3): absolute magnitude on the band 2, band 1 – band 2 color and band 2 – band 3 color, offers precision above 90% for star-forming and passive galaxies. In addition, using the new diagnostic, we classify 68.7% of the galaxies found in the HECATE catalog. Finally, we estimate the activity demographics in the local Universe based on the results from the classification of the full HECATE catalog.
Language English
Issue date 2022-04-01
Collection   School/Department--School of Sciences and Engineering--Department of Physics--Graduate theses
  Type of Work--Graduate theses
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