Hunting for open clusters in \textit{Gaia} DR2: $582$ new OCs in the
  Galactic disc
  Open clusters are key targets for both Galaxy structure and evolution and
stellar physics studies. Since \textit{Gaia} DR2 publication, the discovery of
undetected clusters has proven that our samples were not complete. Our aim is
to exploit the Big Data capabilities of machine learning to detect new open
clusters in \textit{Gaia} DR2, and to complete the open cluster sample to
enable further studies on the Galactic disc. We use a machine learning based
methodology to systematically search in the Galactic disc, looking for
overdensities in the astrometric space and identifying them as open clusters
using photometric information. First, we use an unsupervised clustering
algorithm, DBSCAN, to blindly search for these overdensities in \textit{Gaia}
DR2 $(l,b,\varpi,\mu_{\alpha^*},\mu_\delta)$. After that, we use a deep
learning artificial neural network trained on colour-magnitude diagrams to
identify isochrone patterns in these overdensities, and to confirm them as open
clusters. We find $582$ new open clusters distributed along the Galactic disc,
in the region $|b| < 20$. We can detect substructure in complex regions, and
identify the tidal tails of a disrupting cluster UBC~$274$ of $\sim 3$ Gyr
located at $\sim 2$ kpc. Adapting the methodology into a Big Data environment
allows us to target the search driven by physical properties of the open
clusters, instead of being driven by its computational requirements. This blind
search for open clusters in the Galactic disc increases in a $45\%$ the number
of known open clusters.
