Parts | Days | Selection | Search | Updates | Downloads | Help

T: Fachverband Teilchenphysik

T 88: Top Quarks: Production (Exp.) 3

T 88.2: Talk

Thursday, March 24, 2022, 16:30–16:45, T-H19

Machine learning approaches for parameter reweighting in MC samples of top quark production — •Valentina Guglielmi, Katerina Lipka, and Simone Amoroso — DESY, Hamburg, Germany

In high-energy particle physics, complex Monte Carlo simulations are needed to connect the theory to measurable quantities. Often, the significant computational cost of these programs becomes a bottleneck in physics analyses.

In this contribution, we evaluate an approach based on a Deep Neural Network to reweight simulations to different models or model parameters, 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.

We test the method on Monte Carlo simulations of top quark pair production, that we reweight to different SM parameter values and to different QCD models.