To accurately describe data, tuning the parameters of MC event Generators is essential. At first, experts performed tunings manually based on their sense of physics and goodness of fit. The software, Professor, made tuning more objective by employing polynomial surrogate functions to model the relationship between generator parameters and experimental observables (inner-loop optimization), then optimizing an objective function to obtain generator parameters (outer-loop optimization). Finally, Apprentice, a purely python-based tool, was developed to leverage High-Performance Computing and introduced rational approximation as an alternative surrogate function. However, none of these tuning methods includes MC systematic uncertainties. More importantly, the estimated uncertainties of tuned parameters are unreliable because the objective distribution does not match a chi-squared distribution, and one has to manually set a cutoff threshold on the objective function using educated guesses. In this work, we integrate the MC systematic uncertainties into the inner-loop optimization and outer-loop optimization. With our new method, we find that the objective function nicely follows the chi-square distribution; thus, the uncertainty of the tuned generator parameters is better quantified.
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