Data driven background estimation in HEP using generative adversarial networks

Victor Lohezic; Mehmet Ozgur Sahin; Fabrice Couderc; Julie Malcles

doi:10.1140/epjc/s10052-023-11347-8

2024 Impact factor 4.8

Particles and Fields

Open Access

Eur. Phys. J. C (2023) 83: 256
https://doi.org/10.1140/epjc/s10052-023-11347-8

Regular Article - Experimental Physics

Data driven background estimation in HEP using generative adversarial networks

Victor Lohezic^a, Mehmet Ozgur Sahin, Fabrice Couderc and Julie Malcles

IRFU, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France

^a victor.lohezic@cern.ch

Received: 9 December 2022
Accepted: 17 February 2023
Published online: 27 March 2023

Abstract

Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the $γ + jets$ $\gamma + \textrm{jets}$ background of the $H \to γ γ$ $\textrm{H}\rightarrow \gamma \gamma$ analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event.

© The Author(s) 2023

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