Discovery of nine new stellar groups in the Orion complex.

We use two unsupervised machine learning algorithms, Shared Nearest Neighbor (SNN) and \textit{EnLink}, as a single approach to identify stellar groupings in the Orion star-forming complex as an application to the 5-dimensional astrometric data from Gaia DR2. The algorithms present two different ways to limit user bias when evaluating the relative weights among the astrometric parameters, automatically determined by the machine and through a standard procedure by monitoring several outcome measures. Both algorithms complement each other and produce similar stellar groups. Because SNN groups have a much smaller spread in proper motions compared to \textit{EnLink}, we use \textit{EnLink}, which requires no input, as a first pass tool for group identification and validation. We then used the SNN algorithm to dissect the Orion star-forming complex. We identify 21 spatially- and kinematically-coherent groups in the Orion complex, nine of which previously unknown. The groups show a wide distribution of distances extending as far as about 150 pc in front of the star-forming Orion clouds, to about 50 pc beyond them where we find, unexpectedly, three groups. Our results expose to view the wealth of sub-structure in the OB association, within and beyond the classical Blaauw Orion OBI sub-groups. A full characterization of the new groups is of the essence as it offers the potential to unveil how star formation proceeds globally in large complexes such as Orion. The data and code that generated the groups is provided in this Letter.