Monte Carlo tuning and generator validation
Andy Buckley (Durham U., IPPP), Hendrik Hoeth (Lund U., Dept. Theor., Phys.), Heiko Lacker (Humboldt U., Berlin), Holger Schulz (Humboldt U.,, Berlin), Eike von Seggern (Humboldt U., Berlin)
TL;DR
This paper details a Monte Carlo generator tuning strategy and tools, presenting new Pythia 6.4 tunes based on various experimental data, and compares these to existing and recent tunes.
Contribution
It introduces a comprehensive tuning strategy and tools, along with new Pythia 6.4 tunes validated against multiple experimental datasets.
Findings
New Pythia 6.4 tunes outperform previous tunes in certain observables.
The tuning strategy improves generator accuracy across different experimental conditions.
Comparison shows the new tunes are competitive with Peter Skands' Perugia tunes.
Abstract
We present the Monte Carlo generator tuning strategy followed, and the tools developed, by the MCnet CEDAR project. We also present new tuning results for the Pythia 6.4 event generator which are based on event shape and hadronisation observables from e+e- experiments, and on underlying event and minimum bias data from the Tevatron. Our new tunes are compared to existing tunes and to Peter Skands' new "Perugia" tunes.
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Taxonomy
TopicsAdvanced Measurement and Metrology Techniques · Sensor Technology and Measurement Systems · Scientific Measurement and Uncertainty Evaluation