Cross sections for neutron-induced reactions from surrogate data: revisiting the Weisskopf-Ewing approximation for $(n,n^{\prime})$ and $(n,2n)$ reactions

TL;DR

This paper evaluates the Weisskopf-Ewing approximation's effectiveness in deriving neutron-induced reaction cross sections from surrogate data, emphasizing the need for detailed reaction mechanism modeling for accuracy.

Contribution

It critically assesses the approximation's limitations for $(n,n^{ extprime})$ and $(n,2n)$ reactions, highlighting the necessity of detailed surrogate reaction models.

Findings

Peak cross sections can be estimated with the approximation.

Shape of cross sections at low energies cannot be reliably predicted.

Detailed reaction mechanism modeling is essential for accurate results.

Abstract

Earlier work has demonstrated that cross sections for neutron-induced fission and radiative neutron capture can be determined from a combination of surrogate reaction data and theory. For the fission case, it was shown that Weisskopf-Ewing approximation, which significantly simplifies the implementation of the surrogate method, can be employed. Capture cross sections cannot be obtained, and require a detailed description of the surrogate reaction process. In this paper we examine the validity of the Weisskopf-Ewing approximation for determining unknown $(n,n^{\prime})$ and $(n,2n)$ cross sections from surrogate data. We find that peak cross sections can be estimated using the Weisskopf-Ewing approximation, but the shape of the $(n,n^{\prime})$ and $(n,2n)$ cross sections, especially for low neutron energies, cannot be reliably determined without accounting for the angular-momentum differences between the neutron-induced and surrogate reaction. To obtain reliable $(n,n^{\prime})$ and $(n,2n)$ cross sections from surrogate reaction data, a detailed description of the surrogate reaction mechanisms is required. To do so for the compound-nucleus energies and decay channels relevant to these reactions, it becomes necessary to extend current modeling capabilities.