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May 8 – 12, 2023
Norfolk Waterside Marriott
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Data driven background estimation in HEP using Generative Adversarial Networks

May 9, 2023, 11:45 AM
Hampton Roads Ballroom VIII (Norfolk Waterside Marriott)

Hampton Roads Ballroom VIII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 6 - Physics Analysis Tools Track 6 - Physics Analysis Tools


Lohezic, Victor (Irfu, CEA Saclay - Université Paris-Saclay)


Data-driven methods are widely used to overcome shortcomings of Monte Carlo (MC) 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 generative adversarial networks (GAN) for this task, as GAN are among the best performing generator models for various machine learning applications. The method is illustrated by generating a new misidentified photon for the $\mathrm{\gamma+Jets}$ background of the $\mathrm{H\rightarrow\gamma\gamma}$ analysis at the CERN LHC, thanks to CMS Open Data simulated samples. We demonstrate that the GAN is able to generate a coherent object within the region of interest and still correlated with the different properties of the rest of the event.

Consider for long presentation Yes

Primary authors

Lohezic, Victor (Irfu, CEA Saclay - Université Paris-Saclay) Dr Sahin, Özgür (Irfu, CEA Saclay) Dr Couderc, Fabrice (Irfu, CEA Saclay) Dr Malclès, Julie (Irfu, CEA Saclay)

Presentation materials