Computing the QRPA Level Density with the Finite Amplitude Method

TL;DR

This paper introduces a novel algorithm leveraging the Finite Amplitude Method and Kernel Polynomial Method to efficiently estimate nuclear vibrational level densities with quantifiable accuracy, suitable for parallel computation.

Contribution

The paper presents a new, scalable algorithm combining FAM and Kernel Polynomial Method for accurate vibrational level density estimation in nuclei.

Findings

Provides an estimator with an upper bound on relative error.

Algorithm is highly parallelizable and scales with processing units.

Achieves accurate vibrational level density estimates.

Abstract

We describe a new algorithm to calculate the vibrational nuclear level density of an atomic nucleus. Fictitious perturbation operators that probe the response of the system are generated by drawing their matrix elements from some probability distribution function. We use the Finite Amplitude Method to explicitly compute the response for each such sample. With the help of the Kernel Polynomial Method, we build an estimator of the vibrational level density and provide the upper bound of the relative error in the limit of infinitely many random samples. The new algorithm can give accurate estimates of the vibrational level density. Since it is based on drawing multiple samples of perturbation operators, its computational implementation is naturally parallel and scales like the number of available processing units.