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High dimensional parameter tuning for event generators

Bellm, Johannes LU and Gellersen, Leif LU (2020) In European Physical Journal C 80(1).
Abstract

Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data is crucial. However, the simultaneous optimization of many parameters is computationally challenging. We present an algorithm that allows to tune Monte Carlo Event Generators for high dimensional parameter spaces. To achieve this we first split the parameter space algorithmically in subspaces and perform a Professor tuning on the subspaces with binwise weights to enhance the influence of relevant observables. We test the algorithm in ideal conditions and in real life examples including... (More)

Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data is crucial. However, the simultaneous optimization of many parameters is computationally challenging. We present an algorithm that allows to tune Monte Carlo Event Generators for high dimensional parameter spaces. To achieve this we first split the parameter space algorithmically in subspaces and perform a Professor tuning on the subspaces with binwise weights to enhance the influence of relevant observables. We test the algorithm in ideal conditions and in real life examples including tuning of the event generators Herwig 7 and Pythia 8 for LEP observables. Further, we tune parts of the Herwig 7 event generator with the Lund string model.

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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
European Physical Journal C
volume
80
issue
1
article number
54
publisher
Springer
external identifiers
  • scopus:85078148654
ISSN
1434-6044
DOI
10.1140/epjc/s10052-019-7579-5
language
English
LU publication?
yes
id
2bf8e46a-2adf-4fa3-a786-7c94b3014d60
date added to LUP
2020-02-10 12:03:37
date last changed
2024-03-20 04:45:12
@article{2bf8e46a-2adf-4fa3-a786-7c94b3014d60,
  abstract     = {{<p>Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data is crucial. However, the simultaneous optimization of many parameters is computationally challenging. We present an algorithm that allows to tune Monte Carlo Event Generators for high dimensional parameter spaces. To achieve this we first split the parameter space algorithmically in subspaces and perform a Professor tuning on the subspaces with binwise weights to enhance the influence of relevant observables. We test the algorithm in ideal conditions and in real life examples including tuning of the event generators Herwig 7 and Pythia 8 for LEP observables. Further, we tune parts of the Herwig 7 event generator with the Lund string model.</p>}},
  author       = {{Bellm, Johannes and Gellersen, Leif}},
  issn         = {{1434-6044}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Springer}},
  series       = {{European Physical Journal C}},
  title        = {{High dimensional parameter tuning for event generators}},
  url          = {{http://dx.doi.org/10.1140/epjc/s10052-019-7579-5}},
  doi          = {{10.1140/epjc/s10052-019-7579-5}},
  volume       = {{80}},
  year         = {{2020}},
}