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Article Dans Une Revue European Physical Journal C: Particles and Fields Année : 2023

Data driven background estimation in HEP using Generative Adversarial Networks

Résumé

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 $\gamma + \mathrm{jets}$ background of the $\mathrm{H}\to\gamma\gamma$ 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.

Dates et versions

hal-03909592 , version 1 (21-12-2022)

Identifiants

Citer

Victor Lohezic, Mehmet Ozgur Sahin, Fabrice Couderc, Julie Malcles. Data driven background estimation in HEP using Generative Adversarial Networks. European Physical Journal C: Particles and Fields, 2023, 83 (3), pp.256. ⟨10.1140/epjc/s10052-023-11347-8⟩. ⟨hal-03909592⟩
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