  We introduce a new high dimensional algorithm for efficiency corrected,
maximally Monte Carlo event generator independent fiducial measurements at the
LHC and beyond. The approach is driven probabilistically using a Deep Neural
Network on an event-by-event basis, trained using detector simulation and even
only pure phase space distributed events. This approach gives also a glimpse
into the future of high energy physics, where experiments publish new type of
measurements in a radically multidimensional way.
