Göttingen 2025 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 83: Methods in Particle Physics IV (Lepton Reconstruction)

T 83.10: Vortrag

Donnerstag, 3. April 2025, 18:30–18:45, VG 4.101

Estimation of Non-Prompt Lepton Backgrounds with Classical and Machine Learning Techniques — Korn Steffen, Quadt Arnulf, and •Schiel Nico — II. Physikalisches Institut, Georg- August-Universität Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen

Non-prompt leptons are a significant background in many particle physics analyses, for example tt and HWW^* analyses. These processes depend on the modelling of parton showers and are therefore challenging to predict theoretically. Consequently, data-driven approaches are utilised to model backgrounds arising from non-prompt leptons. Often, classical methods such as the fake-factor method are used. However, machine learning based methods such as normalising flows also show promising results for modelling non-prompt leptons. In this talk, both approaches are compared with respect to their performance.

Keywords: Fake lepton estimation; Machine learning techniques; Normalising Flows