Poster abstract details

The nearby Magellanic Cloud system (MCs) covers tens of square degrees on
the sky. Much of it has been mapped across the electromagnetic spectrum at
high angular resolution and sensitivity - X-rays (XMM), optical (SMASH),
IR (VISTA, WISE, Spitzer, Herschel), radio (ATCA, ASKAP). This provides a
superb dataset to explore the galaxy populations in the background of the
MCs. Our project focusses 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 discovery of
a unique AGN. We will apply artificial intelligence to mining this vast
multi-dimensional space (x,y,z,t,lambda) through a combination of critical
source extraction, association and characterisation with machine learning
search algorithms, within a robust statistical framework. Thus we also
build readiness to fully exploit future generations of all-sky surveys
(LSST, Euclid) when the Big Data challenge is destined to hit home hard.