ATTENTION/WARNING - NE PAS DÉPOSER ICI/DO NOT SUBMIT HERE

Ceci est la version de TEST de DIAL.mem. Veuillez ne pas soumettre votre mémoire sur ce site mais bien à l'URL suivante: 'https://thesis.dial.uclouvain.be'.
This is the TEST version of DIAL.mem. Please use the following URL to submit your master thesis: 'https://thesis.dial.uclouvain.be'.
 

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

Kaczmarczyk, Brieux
(2024)

Files

Master_s_thesis_cGAN_CMS.pdf
  • Open access
  • Adobe PDF
  • 6.56 MB
Download

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.

Citations

  • APA

Kaczmarczyk, B. (2024). Applicability of nonparametric data-driven background modelling using conditional probabilities for CMS data analysis. https://dial-mem.test.bib.ucl.ac.be/handle/2078.2/39618