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@ARTICLE{Rosenkvist:363954,
author = {Rosenkvist, Carl B. and Elfner, Hannah},
title = {{S}ystematic optimization of resonance parameters in a
transport approach},
journal = {Physical review / C},
volume = {112},
number = {1},
issn = {2469-9985},
address = {Woodbury, NY},
publisher = {Inst.},
reportid = {GSI-2026-00157, 2503.05504},
pages = {014911},
year = {2025},
note = {"Published by the American Physical Society under the terms
of the Creative Commons Attribution 4.0 International
license. Further distribution of this work must maintain
attribution to the author(s) and the published article’s
title, journal citation, and DOI. Funded by SCOAP 3 ."},
abstract = {This study optimizes resonance parameters responsible for
strangeness production in the SMASH (Simulating Many
Accelerated Strongly-interacting Hadrons) transport model
using a genetic algorithm. By fitting resonance parameters
to experimental data on exclusive strangeness cross sections
at low energies, we significantly improve the model's
accuracy, notably in pion-proton interactions. Our approach
explores how machine-learning tools can be used for precise
resonance tuning in transport approaches.},
cin = {TES},
ddc = {530},
cid = {I:(DE-Ds200)TES-20160901OR397},
pnm = {612 - Cosmic Matter in the Laboratory (POF4-612) / FIAS -
Frankfurt Institute for Advanced Studies (FIAS) /
SUC-GSI-Frankfurt - Strategic university cooperation GSI-U
Frankfurt/M (SUC-GSI-FR)},
pid = {G:(DE-HGF)POF4-612 / G:(DE-Ds200)FIAS /
G:(DE-Ds200)SUC-GSI-FR},
experiment = {$EXP:(DE-Ds200)no_experiment-20200803$},
typ = {PUB:(DE-HGF)16},
UT = {WOS:001542164000001},
doi = {10.1103/6nmy-s74w},
url = {https://repository.gsi.de/record/363954},
}