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

DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC