Systematic event generator tuning for the LHC

TL;DR

This paper introduces Professor, a systematic method for tuning Monte Carlo event generator parameters to experimental data, improving the accuracy of simulations for LHC experiments.

Contribution

The paper presents a new program, Professor, for parameter tuning of event generators using a response parameterization and optimization approach.

Findings

Substantial improvements over existing tunes.

Application to Pythia 6 with LEP/SLD and Tevatron data.

Recommended as baseline tunes for LHC experiments.

Abstract

In this article we describe Professor, a new program for tuning model parameters of Monte Carlo event generators to experimental data by parameterising the per-bin generator response to parameter variations and numerically optimising the parameterised behaviour. Simulated experimental analysis data is obtained using the Rivet analysis toolkit. This paper presents the Professor procedure and implementation, illustrated with the application of the method to tunes of the Pythia 6 event generator to data from the LEP/SLD and Tevatron experiments. These tunes are substantial improvements on existing standard choices, and are recommended as base tunes for LHC experiments, to be themselves systematically improved upon when early LHC data is available.