Speaker
Description
Data-driven methods are widely used to overcome shortcomings of Monte Carlo (MC) simulations (lack of statistics, mismodeling of processes, etc.) in experimental High Energy Physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use generative adversarial networks (GAN) for this task, as GAN are among the best performing generator models for various machine learning applications. The method is illustrated by generating a new misidentified photon for the $\mathrm{\gamma+Jets}$ background of the $\mathrm{H\rightarrow\gamma\gamma}$ analysis at the CERN LHC, thanks to CMS Open Data simulated samples. We demonstrate that the GAN is able to generate a coherent object within the region of interest and still correlated with the different properties of the rest of the event.
| Consider for long presentation | Yes |
|---|
