New developments in event generator tuning techniques

TL;DR

This paper introduces enhancements to the Professor system for Monte Carlo event generator tuning, enabling faster, more reliable parameter fitting and uncertainty estimation, which improves the efficiency of collider data analysis.

Contribution

The paper presents novel extensions to the Professor tuning system, allowing for rapid analytic modeling, robust uncertainty estimation, and assessment of new data impacts in MC generator tuning.

Findings

First-time robust uncertainty estimates for generator tunings

Efficient fitting process reduces computational resources

Ability to evaluate the impact of new data on tuning quality

Abstract

Data analyses in hadron collider physics depend on background simulations performed by Monte Carlo (MC) event generators. However, calculational limitations and non-perturbative effects require approximate models with adjustable parameters. In fact, we need to simultaneously tune many phenomenological parameters in a high-dimensional parameter-space in order to make the MC generator predictions fit the data. It is desirable to achieve this goal without spending too much time or computing resources iterating parameter settings and comparing the same set of plots over and over again. We present extensions and improvements to the MC tuning system, Professor, which addresses the aforementioned problems by constructing a fast analytic model of a MC generator which can then be easily fitted to data. Using this procedure it is for the first time possible to get a robust estimate of the uncertainty of generator tunings. Furthermore, we can use these uncertainty estimates to study the effect of new (pseudo-) data on the quality of tunings and therefore decide if a measurement is worthwhile in the prospect of generator tuning. The potential of the Professor method outside the MC tuning area is presented as well.