Adversarial Neural Network-based data-simulation corrections for jet-tagging at CMS

Journal of Physics Conference Series, vol. 1525, pp. 012094

Abstract

Abstract Variable-dependent scale factors are commonly used in HEP to improve shape agreement of data and simulation. The choice of the underlying model is of great importance, but often requires a lot of manual tuning e.g. of bin sizes or fitted functions. This can be alleviated through the use of neural networks and their inherent powerful data modeling capabilities. We present a novel and generalized method for producing scale factors using an adversarial neural network. This method is investigated in the context of the bottom-quark jet-tagging algorithms within the CMS experiment. The primary network uses the jet variables as inputs to derive the scale factor for a single jet. It is trained through the use of a second network, the adversary, which aims to differentiate between the data and rescaled simulation.

Authors 5

1. Martin Erdmann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. D. Noll Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Yannik Alexander Rath Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. M. Rieger Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. David Josef Schmidt Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University