DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC

TL;DR

This paper presents a novel high-dimensional algorithm utilizing deep neural networks for efficiency correction in LHC measurements, enabling more precise, Monte Carlo independent fiducial analyses in complex multidimensional spaces.

Contribution

The paper introduces a deep learning-based method for efficiency correction that is independent of Monte Carlo simulations and applicable to high-dimensional event data at the LHC.

Findings

Effective efficiency correction in high dimensions

Monte Carlo independent measurement capability

Potential for multidimensional data analysis in future experiments

Abstract

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.