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HK: Fachverband Physik der Hadronen und Kerne
HK 44: Heavy-Ion Collisions and QCD Phases IX
HK 44.5: Talk
Wednesday, March 13, 2024, 16:45–17:00, HBR 62: EG 03
Constraining Strangeness Production with Machine Learning — •Carl Rosenkvist2 and Hannah Elfner1,2,3 — 1GSI Helmholtzzentrum für Schwerionenforschung, Planckstr. 1, 64291 Darmstadt, Germany — 2Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, 60438 Frankfurt am Main, Germany — 3Institute for Theoretical Physics, Goethe University, Max-von-Laue-Strasse 1, 60438 Frankfurt am Main, Germany
In heavy-ion collisions, strange particles, which are absent in normal matter, are produced during or shortly after the collision, making them vital probes for understanding the underlying physics.
This project focuses on investigating strangeness production using the SMASH (Simulating Many Accelerated Strongly-interacting Hadrons) model. At lower collision energies, SMASH incorporates short-lived particles, known as resonances, to describe the production of strange particles through resonance decay.
However, the properties of resonance particles have uncertainties from experimental measurements, affecting simulations of low-energy observables sensitive to strangeness production. To address this, we employ machine learning algorithms and emulators to fit numerous resonance parameters simultaneously to experimental data, mainly exclusive elementary cross-sections.
Additionally, a recent study comparing SMASH with experimental data on pion beams colliding with carbon and tungsten revealed significant deviations. To understand this observed discrepancy, we will also investigate strangeness production in pion-nuclei collisions.
Keywords: Strangeness Production; Machine Learning; Heavy-ion