  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.
