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Applicability of nonparametric data-driven background modelling using conditional probabilities for CMS data analysis.

Kaczmarczyk, Brieux
(2024)

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Details

Supervisors
Delaere, Christophe ; Bethani, Agni
Faculty
Faculté des sciences
Degree label
Master [120] en sciences physiques, à finalité approfondie
Abstract
A new approach is introduced for background modelling in di-Higgs physics at CMS. This method aims to address the current challenges in modelling Drell-Yan events, which pose a significant background for the di-Higgs physics signature being investigated in the bbWW channel. The approach involves using a modified version of a generative adversarial network (GAN), specifically a conditional GAN (cGAN). Our focus was on generating three physical quantities, using different training data samples: a set of Drell-Yan events generated with MadGraph and a sample of CMS data from 2022. We then evaluated the network's performance by visualizing and statistically analyzing how well the generated observables matched the input samples.