Adversarial Neural Network-based data-simulation corrections for jet-tagging at CMS. (April 2020)

• Record Type: Journal Article
• Authors: Erdmann, Martin; Fischer, Benjamin; Noll, Dennis; Alexander Rath, Yannik; Rieger, Marcel; Josef Schmidt, David
• Is Part Of: Journal of physics. Volume 1525(2020)
• Publication Date: 2020-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1525/1/012094
• Languages: English
• ISSNs: 1742-6588

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.