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Surrogate models for linear response

L. Jin1,*, A. Ravlić1,2,†, P. Giuliani1,‡, K. Godbey1,§, and W. Nazarewicz1,3,∥

  • *Contact author: jinl@frib.msu.edu
  • †Contact author: ravlic@frib.msu.edu
  • ‡Contact author: giulianp@frib.msu.edu
  • §Contact author: godbey@frib.msu.edu
  • ∥Contact author: witek@frib.msu.edu
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Phys. Rev. Research 7, 043347 – Published 30 December, 2025

DOI: https://doi.org/10.1103/vvxs-3mnk

Abstract

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of Yb180 and the β-decay half-life of Ni80, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

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Phys. Rev. Research 7, 043347– Published 30 December, 2025
  • Received 15 October 2025
  • Accepted 11 December 2025
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