Hadronization Model Tuning in GENIE v3
J\'ulia Tena-Vidal, Costas Andreopoulos, Christopher Barry, Steve, Dennis, Steve Dytman, Hugh Gallagher, Steven Gardiner, Walter Giele, Robert, Hatcher, Or Hen, Igor D. Kakorin, Konstantin S. Kuzmin, Anselmo Meregaglia,, Vadim A. Naumov, Afroditi Papadopoulou, Marco Roda
TL;DR
This paper presents a new tuning of the hadronization model in GENIE v3 using charged multiplicity data from bubble chamber experiments, including uncertainty estimates and dataset tension analysis.
Contribution
It introduces the first hadronization tune on averaged charged multiplicity data and assesses parameter uncertainties and dataset tensions.
Findings
Successful hadronization model tuning with uncertainty estimation
Identification of tensions between hydrogen and deuterium datasets
Enhanced modeling accuracy for neutrino-induced hadronization
Abstract
The GENIE neutrino Monte Carlo describes neutrino-induced hadronization with an effective model, known as AGKY, which is interfaced with PYTHIA at high invariant mass. Only the low-mass AGKY model parameters were extracted from hadronic shower data from the FNAL 15 ft and BEBC experiments. In this paper, the first hadronization tune on averaged charged multiplicity data from deuterium and hydrogen bubble chamber experiments is presented, with a complete estimation of parameter uncertainties. A partial tune on deuterium data only highlights the tensions between hydrogen and deuterium datasets.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.