PoS - Proceedings of Science
Volume 449 - The European Physical Society Conference on High Energy Physics (EPS-HEP2023) - T07 Top and Electroweak Physics
Machine learning approaches for parameter reweighting in MC samples of top quark production in CMS
V. Guglielmi
Full text: pdf
Pre-published on: January 20, 2024
Published on: March 21, 2024
In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured data. Many MC samples have to be generated to account for theoretical systematic uncertainties, at a significant computational cost. Therefore, the MC statistic becomes a limiting factor for most measurements and the significant computational cost of these programs a bottleneck in most physics analyses. In this contribution, the Deep neural network using Classification for Tuning and Reweighting (DCTR) approach is evaluated for the reweighting of two systematic uncertainties in MC simulations of top quark pair production within the CMS experiment. DCTR is a method, based on a Deep Neural Network (DNN) technique, to reweight simulations to different model parameters by using the full kinematic information in the event. This methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample.
DOI: https://doi.org/10.22323/1.449.0325
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