Göttingen 2025 – wissenschaftliches Programm

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T: Fachverband Teilchenphysik

T 93: Top Physics IV (Misc.)

T 93.1: Vortrag

Freitag, 4. April 2025, 09:00–09:15, VG 1.103

Studying Machine Learning Techniques to Improve Statistical Precision of Monte Carlo Samples in Top Quark Measurements — •Lennert Griesing, Hartmut Stadie, Peter Schleper, and Johannes Lange — Institute of Experimental Physics, Hamburg University, Germany

Precise measurements of top quark properties at the Large Hadron Collider (LHC) are crucial for testing the Standard Model and exploring new physics. In these measurements, Monte Carlo (MC) simulations are needed to compare theoretical predictions with experimental observables. To account for systematic uncertainties, MC samples are generated for different model parameters. Due to computational costs, these samples are produced with fewer events than the large default simulation sample. Thus, the smaller sample size limits their statistical precision and poses a challenge for nuisance parameter fits. A possible solution is to modify the large default simulation sample using machine learning techniques (ML) so that their distributions reflect the variations in the different model parameters. The aim is to evaluate the precision, accuracy, and potential biases introduced by applying these ML techniques to MC simulations of top quark pair production within the CMS experiment.