تقرير
Data driven background estimation in HEP using Generative Adversarial Networks
| العنوان: | Data driven background estimation in HEP using Generative Adversarial Networks |
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| المؤلفون: | Lohezic, Victor, Sahin, Mehmet Ozgur, Couderc, Fabrice, Malcles, Julie |
| المصدر: | Eur. Phys. J. C 83, 256 (2023) |
| سنة النشر: | 2022 |
| المجموعة: | High Energy Physics - Experiment Physics (Other) |
| مصطلحات موضوعية: | High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1140/epjc/s10052-023-11347-8 |
| URL الوصول: | http://arxiv.org/abs/2212.03763 |
| رقم الأكسشن: | edsarx.2212.03763 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1140/epjc/s10052-023-11347-8 |
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