Speaker
Description
As the LHC moves into its high-luminosity phase, the CMS experiment must handle increasingly complex data collected at much higher rates. To complement real data, simulated samples must also scale in volume and complexity while meeting the growing demands of the CMS physics program. Increased use of the CMS fast Monte Carlo production framework (FastSim) can help meet these demands, particularly if its accuracy is improved. We introduce calibrated classifiers for scale factors (CC4SF), a machine-learning approach that computes scale factors—-weights applied to simulated objects to account for biases in reconstruction and selection efficiency—-in a differential way. Using a multi-dimensional input feature space, we showcase CC4SF for the example of FastSim electron, muon, and tau lepton scale factors, which serve to correct FastSim's efficiency modeling so that it closely matches the more accurate but resource-intensive FullSim.