Nuclear data activities for medium mass and heavy nuclei at Los Alamos M. R. Mumpower1T. M. Sprouse1T.Kawano1M. W. Herman1A. E. Lovell1G. W. Misch1D.Neudecker1H. Sasaki1I.Stetcu1 and P .Talou1

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Nuclear data activities for medium mass and heavy nuclei at Los Alamos

M. R. Mumpower1,∗,T. M. Sprouse1,T. Kawano1,M. W. Herman1,A. E. Lovell1,G. W. Misch1,D. Neudecker1,H.

Sasaki1,I. Stetcu1, and P. Talou1

1Los Alamos National Laboratory, Los Alamos, NM, 87545, USA

Abstract. Nuclear data is critical for many modern applications from stockpile stewardship to cutting edge

scientiﬁc research. Central to these pursuits is a robust pipeline for nuclear modeling as well as data assimilation

and dissemination. We summarize a small portion of the ongoing nuclear data eﬀorts at Los Alamos for medium

mass to heavy nuclei. We begin with an overview of the NEXUS framework and show how one of its modules

can be used for model parameter optimization using Bayesian techniques. The mathematical framework aﬀords

the combination of diﬀerent measured data in determining model parameters and their associated correlations.

It also has the advantage of being able to quantify outliers in data. We exemplify the power of this procedure

by highlighting the recently evaluated 239Pu cross section. We further showcase the success of our tools and

pipeline by covering the insight gained from incorporating the latest nuclear modeling and data in astrophysical

simulations as part of the Fission In R-process Elements (FIRE) collaboration.

1 Introduction

Nuclear data have a profound impact on modern society

[1]. These data serve as a foundation for nuclear energy,

nuclear security, and the wide variety of research that is

carried out in nuclear astrophysics from the composition

of neutron stars to the impact of kilonova light curves [2–

5].

Despite its importance, a robust pipeline for nuclear

data assimilation and dissemination still remains on the

horizon. Currently, major evaluated databases of nuclear

reactions, decays and structure can be found in ENDF

[6] and ENSDF [7]. Inputs valuable for reaction model-

ing codes can be found in the Reference Input Parame-

ter Library (RIPL) [8] and experimental reaction data can

be found in EXFOR [9]. The Atomic Mass Evaluation

(AME) [10, 11] and NuBase [12] provide targeted infor-

mation regarding ground state and decay properties.

The disparate nature of these sources, coupled with

the diﬃculty of extracting information, complicate the

use of these database in modern applications, particu-

larly with cutting edge science. In this contribution, we

provide a brief overview of the nuclear data activities at

Los Alamos National Laboratory (LANL). We discuss the

NEXUS framework which represents a ﬁrst step towards

providing a pipeline between nuclear data, nuclear mod-

eling eﬀorts and applications. We showcase the utility of

this platform by highlighting its use in a recent evaluation

of 239Pu cross sections as well as in the scientiﬁc Fission

In R-process Elements (FIRE) collaboration. We end with

a discussion of recent tools developed at LANL intended

to advance the use of nuclear data in astrophysical appli-

cations.

∗e-mail: mumpower@lanl.gov

2 NEXUS

The Los Alamos Python package, NEXUS, is a data-

agnostic framework intended to furnish access to nuclear

properties. The phrase ‘data agnostic’ means that it does

not matter how the data was generated or parsed. The out-

put format of the data is also irrelevant to the framework’s

function. Instead the focus of the code is on a consistent

object-oriented representation of various physical quanti-

ties and the relationships between them. This approach

is in stark contrast to the major databases which revolve

around the transmission format of data.

As an example of an object in NEXUS, in its simplest

form a reaction cross section may be reasonably repre-

sented by two arrays: the incident particle energy and the

cross section at each energy. Additional information may

be warranted, in which case the base object may be ex-

tended. There may be uncertainties reported on each en-

ergy point as well as uncertainties in the cross section val-

ues. For particular applications associated metadata may

also need to be aﬃliated with attributes, for example, pro-

viding the physical units of each of the arrays. The focal

point is on the representation of the data in code, not on

how the information will be transmitted.

Information transmission of data is supported however

in NEXUS. This revolves around the concept of marshalling

which is a more general concept of object serialization

(which deals only with string representations). Each ob-

ject in NEXUS has a corresponding object, a marshaller,

which handles its transmutation to various representations.

A marshaller may translate one object type into another,

or it may convert an object from its code representation

to a form that can be saved on a computer hard disk, e.g.

ASCII or JSON. The power of this approach means that

arXiv:2210.12136v1 [nucl-th] 21 Oct 2022

complex derived objects may be constructed and converted

to the desired output format for particular applications. A

concert example is the use of atomic binding energies and

their appropriate parsing into suitable reaction network

codes for use in astrophysical [13] or Machine Learning

applications [14, 15].

The NEXUS framework provides a base set of objects

for nuclear properties that are commonly found in the

aforementioned nuclear databases. In addition, access to

a host of theoretical outputs from nuclear model codes

are also available. Among other data access methods, this

list includes, level densities, γ-strength functions, optical

model parameters, ground state binding energies, and re-

action cross sections. Information regarding nuclear iso-

mers has recently been utilized to study the impact of long-

lived excited states in astrophysics [16, 17].

The data-agnostic approach NEXUS framework also

provides a set of application programming interfaces

(APIs) to nuclear data libraries, nuclear model codes and

application codes. The transition between these three areas

enabled by this platform is depicted pictorially in Figure 1.

NEXUS3

Data Theory

Applications

Figure 1. The Los Alamos Python package, NEXUS, provides a

series of application programming interfaces (APIs) to eﬃciently

go from nuclear theory or data to applications and back again.

3 Hierarchical Approach to Nuclear Data

Evaluations

We now highlight the power of the NEXUS framework

by showcasing a result from a recent evaluation of 239Pu

which uses the Bayesian optimization module. The eval-

uation is based on a hierarchical approach where data is

deemed most important followed by model calculation to

‘ﬁll in the gaps’ when empirical data is lacking. This eval-

uation combines experimental cross section data from EX-

FOR along with output from the Los Alamos statistical

model code, CoH [18, 19].

In order to maintain consistency throughout each eval-

uated cross section channel, a global set of model param-

eters must be ﬁt to available data. Model parameter ﬁt-

ting is performed incrementally channel by channel start-

ing with the total cross section. Because most statistical

model cross sections perform very well with respect to

available data, it is possible to use a Bayesian approach

called hyperparameter optimization to optimize the model

with respect to independent datasets [20].

To optimize the calculated total cross section with re-

spect to available data, σtot, a Metropolis random-walk

algorithm is used to probe the optical model parameter

space. The Soukhovitskii potential is used as the basis

for the optical model [21, 22]. The model space consists

of six parameters, including the potential depths, diﬀuse-

ness and radii. All other model parameters are held ﬁxed

during this optimization as they do not inﬂuence the total

cross section. It was determined that holding the optical

model deformation ﬁxed, rather than letting it vary dur-

ing the optimization was ideal for approximating a robust

minimum. In this procedure the chi-square goodness of ﬁt

is minimized with respect to data which is weighted based

on its uncertainty.

The resultant parameter sensitivities of the ﬁtting pro-

cedure are shown in Figure 2. The optical model param-

eters are standardized to unity to ensure that there is no

diﬀerence in the scale of individual parameters. All of the

model parameters are found to exhibit a relatively strongly

peaked distribution around the optimal value. The opti-

mal values are all within 10% from the baseline parame-

ters suggested by Soukhovitskii with diﬀuseness parame-

ters changing the most.

Figure 2. The determination of optical model parameters us-

ing Bayesian hyperparameter optimization. Shaded regions high-

light areas of minimal χ2.

The cross section ﬁt with the optimal optical model

parameters are shown in Fig. 3. The procedure performs

nearly identically to the previous evaluation of ENDF/B-

VIII.0. Slight modiﬁcations are seen relative to ENDF/B-

VIII.0 below 30 keV where the reported uncertainties of

the datasets pull down the ﬁt to the total cross section. A

similar modiﬁcation, albeit to a much smaller eﬀect, can

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NucleardataactivitiesformediummassandheavynucleiatLosAlamosM.R.Mumpower1;,T.M.Sprouse1,T.Kawano1,M.W.Herman1,A.E.Lovell1,G.W.Misch1,D.Neudecker1,H.Sasaki1,I.Stetcu1,andP.Talou11LosAlamosNationalLaboratory,LosAlamos,NM,87545,USAAbstract.Nucleardataiscriticalformanymodernapplicationsfromstockpilestew...

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Nuclear data activities for medium mass and heavy nuclei at Los Alamos M. R. Mumpower1T. M. Sprouse1T.Kawano1M. W. Herman1A. E. Lovell1G. W. Misch1D.Neudecker1H. Sasaki1I.Stetcu1 and P .Talou1

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