Speaker
Description
Monte Carlo simulations are used extensively in high-energy particle physics analyses. However, imperfections in the configuration of detector simulation can lead to significant discrepancies between simulated events and collision data. Such mismodelling is often addressed using scale factors, which can be accompanied by large systematic uncertainties that compromise the sensitivity of measurements and searches. To mitigate potential biases and uncertainties arising from mismodelling, it is essential to calibrate simulations to data. We present two novel calibration methods based on machine-learning techniques: a reweighting approach, which employs a classifier to learn the ratio of probability density functions between simulation and data, and a normalising-flow approach, which learns a high-dimensional transformation to map simulation to data. Compared to traditional calibration methods, both approaches offer continuous, unbinned corrections across high-dimensional feature spaces, enabling improved global agreement with data. The techniques are demonstrated in the context of correcting the signatures of simulated electromagnetic showers in the CMS calorimeters, using proton-proton collision data collected during 2022 at $\sqrt{s}$ = 13.6 $TeV$, corresponding to an integrated luminosity of 26.7 $fb^{-1}$. The complementary strengths and limitations of the two methods are compared.