DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC
This addresses the need for more accurate and generator-independent measurements in high-energy physics experiments like the LHC.
The paper tackles the problem of efficiency-corrected fiducial measurements at the LHC by introducing a high-dimensional algorithm using a Deep Neural Network, which works on an event-by-event basis and can be trained with detector simulation or pure phase space events.
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.