4.7 Article

Neutrino-nucleon cross-section model tuning in GENIE v3

期刊

PHYSICAL REVIEW D
卷 104, 期 7, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.104.072009

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资金

  1. CC-IN2P3 Computing Center
  2. Particle Physics Department at Rutherford Appleton Laboratory
  3. STFC through LIV.DAT, the Liverpool Big Data Science Centre for Doctoral Training [2021488]
  4. Institute of Particle Physics Phenomenology, University of Durham
  5. Fermi Research Alliance, LLC (FRA) [DE-AC02-07CH11359]
  6. Russian Science Foundation [18-12-00271]
  7. Science and Technology Facilities Council [ST/S000879/1] Funding Source: researchfish
  8. Russian Science Foundation [18-12-00271] Funding Source: Russian Science Foundation

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This study provides a comprehensive summary of the bare-nucleon cross-section tuning within the GENIE global analysis framework, including the tuning of different cross-section model sets and the investigation of tensions between exclusive and inclusive data. The analysis improvements and careful handling of systematics in historic data aim to support the consumers of these physics tunes.
We summarize the results of a study performed within the GENIE global analysis framework, revisiting the GENIE bare-nucleon cross-section tuning and, in particular, the tuning of (a) the inclusive cross section, (b) the cross section of low-multiplicity inelastic channels (single-pion and double-pion production), and (c) the relative contributions of resonance and nonresonance processes to these final states. The same analysis was performed with several different comprehensive cross-section model sets available in GENIE Generator v3. In this work we perform a careful investigation of the observed tensions between exclusive and inclusive data, and install analysis improvements to handle systematics in historic data. All tuned model configurations discussed in this paper are available through public releases of the GENIE Generator. With this paper we aim to support the consumers of these physics tunes by providing comprehensive summaries of our alternate model constructions, of the relevant datasets and their systematics, and of our tuning procedure and results.

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