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Machine learning for correlations of electromagnetic properties in ab initio calculations

Marco Knöll1,*, Marc L. Agel1, Tobias Wolfgruber1, Pieter Maris2, and Robert Roth1,3,†

  • 1Institut für Kernphysik, Fachbereich Physik, Technische Universität Darmstadt, Schlossgartenstrasse 2, 64289 Darmstadt, Germany
  • 2Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA
  • 3Helmholtz Forschungsakademie Hessen für FAIR, GSI Helmholtzzentrum, 64289 Darmstadt, Germany

  • *Contact author: marco.knoell@physik.tu-darmstadt.de
  • †Contact author: robert.roth@physik.tu-darmstadt.de

High-precision ab initio nuclear theory: learning to overcome model-space limitations

Marco Knöll

J. Phys. G: Nucl. Part. Phys. 53 083001 (2026)

Robust ab initio predictions for dimensionless ratios of E2 and radius observables. I. Electric quadrupole moments and deformation

Mark A. Caprio, Pieter Maris, and Patrick J. Fasano

Phys. Rev. C 112 044318 (2025)

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