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arXiv:2609.03903

Reduced-basis method for linear response within nuclear density functional theory

Nobuo Hinohara, Xilin Zhang, Jonathan Engel

Published Sep 3, 2026• 0 citations
arXivHTMLPDF

Abstract

Background: The quasiparticle random-phase approximation (QRPA) within nuclear density functional theory provides a powerful framework for describing collective excitations. Although the finite-amplitude method (FAM) efficiently solves the QRPA problem, repeated calculations for different external-field parameters remain computationally demanding. Purpose: We construct a reduced basis method (RBM)-based emulator for the FAM that treats the complex energy of the external field as a model parameter to efficiently reproduce FAM amplitudes and QRPA eigenmodes. Methods: High-fidelity FAM calculations are performed at a small set of training points in the complex-energy plane. The resulting FAM amplitudes form a non-orthogonal reduced basis. A variational equation yields an emulator that can predict the response at arbitrary complex energies and QRPA eigensolutions without additional full FAM calculations. Results: The RBM emulator accurately reproduces FAM strength distributions in both giant-resonance and low-energy regions when the relevant energy domain is covered by the training set. It also reproduces imaginary QRPA modes associated with shape instabilities of the HFB state. Applied to the $K^π=0^+$ mode of rare-earth Dy isotopes in a realistic model space, the emulator reproduces strength distributions and the lowest $0^+$ collective states with precision comparable to full FAM calculations, reducing the computational cost by more than an order of magnitude. Conclusions: The RBM provides an efficient and accurate FAM emulator. Its ability to reproduce giant-resonance, low-energy, and imaginary-energy modes at drastically reduced computational cost makes it promising for density-functional optimization, calculations of collective inertia, and large-scale surveys of nuclear collective excitations.

DOI
10.48550/arxiv.2609.03903

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Background: The quasiparticle random-phase approximation (QRPA) within nuclear density functional theory provides a powerful framework for describing collective excitations. Although the finite-amplitude method (FAM) efficiently solves the QRPA problem, repeated calculations for different external-field parameters remain computationally demanding. Purpose: We construct a reduced basis method (RBM)-based emulator for the FAM that treats the complex energy of the external field as a model parameter to efficiently reproduce FAM amplitudes and QRPA eigenmodes. Methods: High-fidelity FAM calculations are performed at a small set of training points in the complex-energy plane. The resulting FAM amplitudes form a non-orthogonal reduced basis. A variational equation yields an emulator that can predict the response at arbitrary complex energies and QRPA eigensolutions without additional full FAM calculations. Results: The RBM emulator accurately reproduces FAM strength distributions in both giant-resonance and low-energy regions when the relevant energy domain is covered by the training set. It also reproduces imaginary QRPA modes associated with shape instabilities of the HFB state. Applied to the Kπ=0+K^π=0^+Kπ=0+ mode of rare-earth Dy isotopes in a realistic model space, the emulator reproduces strength distributions and the lowest 0+0^+0+ collective states with precision comparable to full FAM calculations, reducing the computational cost by more than an order of magnitude. Conclusions: The RBM provides an efficient and accurate FAM emulator. Its ability to reproduce giant-resonance, low-energy, and imaginary-energy modes at drastically reduced computational cost makes it promising for density-functional optimization, calculations of collective inertia, and large-scale surveys of nuclear collective excitations.
The quasiparticle random phase approximation or QRPA has been a foundational many-body technique for decades across quantum chemistry, condensed matter and nuclear physics. Although computing power has increased and the matrix-free Finite Amplitude Method (FAM) exists, the computational complexity of FAM-QRPA calculations remains a limiting factor for the generation of linear response data on atomic nuclei that are crucial for several research fields. In this work, we establish that the FAM-QRPA equations are inherently suited to a reduced order modelling framework and can be emulated efficiently. Moreover, we present a greedy snapshot selection strategy that leverages the reduced cost of FAM-QRPA calculations when the imaginary part of the excitation frequency is large. Even when accounting for its construction, the resulting emulator accelerates strength function calculations by significantly more than an order of magnitude. We demonstrate that this framework and its speed-up generalize to light and heavy nuclei, different numerical representations, and diverse nuclear models including chiral EFT and configuration-interaction shell model approaches, as well as Skyrme, Gogny, and relativistic energy density functionals.
In recent years, reduced-basis methods (RBMs) have been adapted to the many-body eigenvalue problem and they have been used, largely in nuclear physics, as fast emulators able to bypass expensive direct computations while still providing highly accurate results. This work is meant to show that the RBM is an efficient and accurate emulator for the strong correlations induced by the pairing interaction in a variety of finite systems like ultrasmall superconducting grains, interacting topological superfluids, and mesoscopic hybrid superconductor-semiconductor devices, all of which require an expensive, beyond-mean-field, particle-number conserving description. These systems are modeled by the number-conserving Richardson pairing Hamiltonian and its appropriate generalizations. Their ground state is solved for exactly using the density matrix renormalization group. The reduced basis is assembled iteratively from a small number of exact ground-state vectors, well-chosen from across the relevant parameter space using a fast estimate of the emulation error and a greedy local optimization algorithm. The RBM emulation is found to accurately describe the weak-to-strong pairing crossover in small grains, the third-order topological phase transition of the interacting Richardson-Kitaev chain, and the complex charge stability diagram of a hybrid quantum dot--superconductor device. For all considered systems the emulation error decreases exponentially with the reduced-basis dimensionality. The number of basis vectors necessary to reach a fixed emulation accuracy only shows a modest linear growth with increasing system size. RBMs are confirmed to be cheap and accurate emulators for the widely encountered superconducting phenomena. Capable of providing orders of magnitude computational speedup with respect to approaches based only on traditional many-body solvers, they open new possibilities in building and solving models of interacting many-body systems and in better interfacing them with experimental design and data analysis.
Reduced-basis methods provide a powerful framework for building efficient and accurate emulators. Although widely applied in many fields to simplify complex models, reduced-basis methods have only been recently introduced into nuclear physics. In this Letter we build an emulator to study the single-particle structure of atomic nuclei. By scaling a suitable mean-field Hamiltonian, a ``universal'' reduced basis is constructed capable of accurately and efficiently reproduce the entire single-particle spectrum of a variety of nuclei. Indeed, the reduced-basis model reproduces both ground- and excited-state energies as well as the associated wave functions with remarkable accuracy. Our results bode well for more demanding applications that use Bayesian optimization to calibrate nuclear energy density functionals.
Abstract The linear response theory is a powerful tool to study properties of quantum many-particle systems. It uses a one-body perturbation field (as a pump) and measures a change in another (or the same) one-body observable (as a probe). In the context of time-dependent density functional theory (TDDFT), the primary target of the linear response is the density response. The finite amplitude method (FAM) significantly reduces both computational cost and coding tasks, and succeeds in facilitating the linear response calculations in a variety of fields in nuclear physics. We start with a review of the linear response theory and present the essence of the FAM theory and various applications, especially in studies of nuclear collective dynamics.
We present the reduced basis method as a tool for developing emulators for equations with tunable parameters within the context of the nuclear many-body problem. The method uses a basis expansion informed by a set of solutions for a few values of the model parameters and then projects the equations over a well-chosen low-dimensional subspace. We connect some of the results in the eigenvector continuation literature to the formalism of reduced basis methods and show how these methods can be applied to a broad set of problems. As we illustrate, the possible success of the formalism on such problems can be diagnosed beforehand by a principal component analysis. We apply the reduced basis method to the one-dimensional Gross-Pitaevskii equation with a harmonic trapping potential and to nuclear density functional theory for 48Ca^{48}\mathrm{Ca}48Ca, achieving speed-ups of more than $\ifmmode\times\else\texttimes\fi{}150$ and $\ifmmode\times\else\texttimes\fi{}250$, respectively, when compared to traditional solvers. The outstanding performance of the approach, together with its straightforward implementation, show promise for its application to the emulation of computationally demanding calculations, including uncertainty quantification.
The validation, verification, and uncertainty quantification of computationally expensive theoretical models of quantum many-body systems require the construction of fast and accurate emulators. In this work, we develop emulators for auxiliary field diffusion Monte Carlo (AFDMC), a powerful many-body method for nuclear systems. We introduce a reduced-basis method (RBM) emulator for AFDMC and study it in the simple case of the deuteron. Furthermore, we compare our RBM emulator with the recently proposed parametric matrix model (PMM) that combines elements of RBMs with machine learning. We contrast these two approaches with a traditional Gaussian Process emulator. All three emulators constructed here are based on a very limited set of 5 training points, as expected for realistic AFDMC calculations, but validated against O ( 10 3 ) exact solutions. We find that the PMM, with emulator errors of only ≈ 0.1 % and speed-up factors of ≈ 10 7 , outperforms our implementation of the other two emulators when applied to AFDMC.