Autotunes splits event generator parameter spaces into correlated subspaces and assigns automatic observable weights, enabling iterative Professor-based tuning in higher dimensions than the standard approach.
The Perugia Tunes
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We present 7 new tunes of the pT-ordered shower and underlying-event model in Pythia 6.4. These "Perugia" tunes update and supersede the older "S0" family. The new tunes include the updated LEP fragmentation and flavour parameters reported on by H. Hoeth at this workshop. The hadron-collider specific parameters were then retuned (manually) using Tevatron min-bias data from 630, 1800, and 1960 GeV, Tevatron Drell-Yan data at 1800 and 1960 GeV, as well as SPS min-bias data at 200, 540, and 900 GeV. In addition to the central parameter set, related tunes exploring systematically soft, hard, parton density, and color structure variations are included. Based on these variations, a best-guess prediction of the charged track multiplicity in inelastic, nondiffractive minimum-bias events at the LHC is made.
citation-role summary
background 1
citation-polarity summary
fields
hep-ph 1years
2019 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
High dimensional parameter tuning for event generators
Autotunes splits event generator parameter spaces into correlated subspaces and assigns automatic observable weights, enabling iterative Professor-based tuning in higher dimensions than the standard approach.