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Discovering exotic AGN behind the Magellanic Clouds

Published online by Cambridge University Press:  29 January 2021

Clara M. Pennock ,
Jacco Th. van Loon ,
Cameron P. M. Bell ,
Miroslav D. Filipović ,
Tana D. Joseph  and
Eleni Vardoulaki
Clara M. Pennock
Affiliation:
Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK email: [email protected]
Jacco Th. van Loon
Affiliation:
Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK email: [email protected]
Cameron P. M. Bell
Affiliation:
Leibniz Institute for Astrophysics Potsdam, Potsdam, Germany
Miroslav D. Filipović
Affiliation:
Western Sydney University, Sydney, Australia
Tana D. Joseph
Affiliation:
University of Manchester, Manchester, UK
Eleni Vardoulaki
Affiliation:
Max-Planck-Institut für Radioastronomie, Bonn, Germany
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Abstract

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The nearby Magellanic Clouds system covers more than 200 square degrees on the sky. Much of it has been mapped across the electromagnetic spectrum at high angular resolution and sensitivity –X-ray (XMM-Newton), UV (UVIT), optical (SMASH), IR (VISTA, WISE, Spitzer, Herschel), radio (ATCA, ASKAP, MeerKAT). This provides us with an excellent dataset to explore the galaxy populations behind the stellar-rich Magellanic Clouds. We seek to identify and characterise AGN via machine learning algorithms on this exquisite data set. Our project focuses not on establishing sequences and distributions of common types of galaxies and active galactic nuclei (AGN), but seeks to identify extreme examples, building on the recent accidental discoveries of unique AGN behind the Magellanic Clouds.

Keywords

AGNMachine-learningMagellanic CloudsMulti-wavelength
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S356: Nuclear Activity in Galaxies Across Cosmic Time , October 2019 , pp. 335 - 338
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

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