arXiv subjects

A. E. Lovell

Publications and source records attributed to A. E. Lovell.

At least 19 recordsLinked to original sources

arXiv2026

Rapid Uncertainty Quantification on a Latent Field using Fisher Information

Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solver to quantify uncertainty in both the inferred field and its predicted observables, using a local Gaussian approximation without sampling the full parameter posterior. Applied to nuclear optical potentials, the method distinguishes features constrained by scattering measurements from those whose uncertainty remains set by the prior. Our flexible model improves agreement with held-out cross sections and achieves nearly nominal coverage with narrower predictive intervals than an established Bayesian optical model.

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arXiv2026

New Procedure for the Evaluation of Fission Product Yields: Application to the Spontaneous Fission of $^{252}$Cf

Over the last decade, there has been significant improvement in the understanding and modeling of the decay of fission fragments by both prompt and delayed emission. These model improvements open the door for performing consistent evaluations across multiple fission observables, providing not only mean values but also covariances between observables. One such model is the Hauser-Feshbach Fission Fragment Decay model implemented in $\texttt{BeoH}$, which uses distributions of initial conditions of fission fragments to perform a Hauser-Feshbach decay for prompt neutron and $γ$-ray emission and evaluated decay data to calculate cumulative fission product yields. This manuscript describes a new evaluation procedure for independent and cumulative fission product yields, including full correlations among the fission products. We use a Bayesian Kalman filter to fit both experimental cumulative fission product yields and those from the ENDF/B-VIII.0 evaluated library, producing mean values and covariances. In addition to comparing the fission products from these optimizations, we calculate prompt and delayed neutron and $γ$-ray multiplicities using the fitted parameters and compare to some available experimental data. We see reasonable agreement, even when these quantities are not included in the optimization.

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arXiv2026

ENDF/B-VIII.1: Updated Nuclear Reaction Data Library for Science and Applications

The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the $^{239}$Pu file was reevaluated by a joint international effort and that updated $^{16,18}$O, $^{19}$F, $^{28-30}$Si, $^{50-54}$Cr, $^{55}$Mn, $^{54,56,57}$Fe, $^{63,65}$Cu, $^{139}$La, $^{233,235,238}$U, and $^{240,241}$Pu neutron nuclear data from the IAEA coordinated INDEN collaboration were adopted. Over 60 neutron dosimetry cross sections were adopted from the IAEA's IRDFF-II library. In addition, the new library includes significant changes for $^3$He, $^6$Li,$^9$Be, $^{51}$V, $^{88}$Sr, $^{103}$Rh, $^{140,142}$Ce, Dy, $^{181}$Ta, Pt, $^{206-208}$Pb, and $^{234,236}$U neutron data, and new nuclear data for the photonuclear, charged-particle and atomic sublibraries. Numerous thermal neutron scattering kernels were reevaluated or provided for the very first time. On the covariance side, work was undertaken to introduce better uncertainty quantification standards and testing for nuclear data covariances. The significant effort to reevaluate important nuclides has reduced bias in the simulations of many integral experiments with particular progress noted for fluorine, copper, and stainless steel containing benchmarks. Data issues hindered the successful deployment of the previous ENDF/B-VIII.0 for commercial nuclear power applications in high burnup situations. These issues were addressed by improving the $^{238}$U and $^{239,240,241}$Pu evaluated data in the resonance region. The new library performance as a function of burnup is similar to the reference ENDF/B-VII.1 library. The ENDF/B-VIII.1 data are available in ENDF-6 and GNDS format at https://doi.org/10.11578/endf/2571019.

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arXiv2026

Physics-based method for generating probability table using random-matrix approach

We develop a new method for generating probability tables based on a solid theoretical foundation. The fluctuating cross sections are calculated using the GOE-$S$-matrix model, in which the Gaussian Orthogonal Ensemble (GOE) is incorporated into the calculation of the scattering ($S$) matrix. The calculated cross sections are then converted into the probability tables in the same manner as in NJOY. Using $^{238}$U and $^{239}$Pu as target nuclei, we determine the optimal model parameters based on the convergence behavior of the average cross sections. The statistical uncertainty of the probability tables is examined as a function of the number of ladders. We demonstrate that the probability tables calculated at 0 K are qualitatively comparable with those calculated using the conventional single-level Breit-Wigner formalism, albeit we observe some local differences due to requisite unitality for the $S$ matrix.

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arXiv2025

Statistical properties of neutron-induced reaction cross sections using random-matrix approach

We investigate the statistical properties of neutron-induced nuclear reactions on $^{238}$U using the GOE-$S$-matrix model, in which the Gaussian Orthogonal Ensemble (GOE) is embedded into the scattering ($S$) matrix. The GOE-$S$-matrix model does not require any experimental values of the average level spacing $D$ and average decay width $Γ$ with their statistical distributions, but the model is fully characterized by the channel transmission coefficients used in the Hauser-Feshbach theory. We demonstrate that the obtained compound nucleus decay width distribution resembles the $χ$-squared distribution with the degree of freedom greater than unity. This approach enables us to generate fluctuating cross sections while preserving requisite unitarity and accounting for interference between resonances. By comparing the calculated cross section distribution with that from $R$-matrix theory, we demonstrate a smooth transition from the resolved resonance region to the continuum region.

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arXiv2025

Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data

We present an extension of the Levenberg-Marquardt algorithm for fitting multichannel nuclear cross section data. Our approach offers a practical and robust alternative to conventional trust-region methods for analyzing experimental data. The CoH$_3$ code, based on the Hauser-Feshbach statistical model, involves a large number of interdependent parameters, making optimization challenging due to the presence of "sloppy" directions in parameter space. To address the uneven distribution of experimental data across reaction channels, we construct a weighted Fisher Information Metric by integrating prior distributions over dataset weights. This framework enables a more balanced treatment of heterogeneous data, improving both parameter estimation and convergence robustness. We show that the resulting weighted Levenberg-Marquardt method yields more physically consistent fits for both raw and smoothed datasets, using experimental data for ${}^{148}$Sm as a representative example. Additionally, we introduce a geometric scaling strategy to accelerate convergence -- a method based on the local geometry of the manifold.

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arXiv2025

An optical-lensing inspired data thinning method for nuclear cross section data

In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel variant of the data thinning algorithm, inspired by the principles of optical lensing, which effectively reduces data volume while preserving critical information. We show how it improves fitting through a toy problem and for several examples of total cross sections for neutron-induced reactions on rare-earth isotopes. We demonstrate how this method can be applied as an efficient pre-processing step prior to smoothing, significantly improving computational efficiency without compromising the quality of uncertainty quantification.

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arXiv2025

Reducing parametric uncertainties through information geometry methods

Information geometry is a study of applying differential geometry methods to challenging statistical problems, such as uncertainty quantification. In this work, we use information geometry to study how measurement uncertainties in pre-neutron emission mass distributions affect the parameter estimation in the Hauser-Feshbach fission fragment decay code, CGMF. We quantify the impact of reduced uncertainties on the pre-neutron mass yield of specific masses to these parameters, for spontaneous fission of ${}^{252}$Cf, first using a toy model assuming Poissonian uncertainties, then an experimental measurement taken from Göök et al., 2014 in EXFOR. We achieved a reduction of up to $\sim15\%$ in CGMF parameter errors, predominantly in $w_0^{(1)}$ and $w_1^{(0)}$.

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arXiv2024

A novel emulator for fission event generators

A wide variety of emulators have been developed for nuclear physics, particularly for use in quantifying and propagating parametric uncertainties to observables. Most of these methods have been used to emulate structure observables, such as energies, masses, or separation energies, or reaction observables, such as cross sections. Rarely, if ever, have event generators for theory models been emulated. Here, we describe one such novel emulator for the fission fragment decay code, $\texttt{CGMF}$, which calculates the emission of prompt neutrons and $γ$ rays from fission fragments. The emulator described in this work uses a combination of a noisy emission model and mixture density network to model the neutron and $γ$-ray multiplicities and energies. In this manuscript, we display the power of this type of emulator for not only modeling average prompt fission observables but also correlations between fission fragment initial conditions and these observables, using both neutron-induced and spontaneous fission reactions.

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arXiv2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics and fundamental symmetries. The long-standing goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of $^{108}$Ru, $^{110}$Ru and $^{116}$Pd were measured to a relative mass precision $δm/m \approx 10^{-8}$ via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. These mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

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arXiv2024

The role of the likelihood for elastic scattering uncertainty quantification

Background: Analyses of elastic scattering with the optical model (OMP) are widely used in nuclear reactions. Purpose: Previous work compared a traditional frequentist approach and a Bayesian approach to quantify uncertainties in the OMP. In this study, we revisit this comparison and consider the role of the likelihood used in the analysis. Method: We compare the Levenberg-Marquardt algorithm for $χ^{2}$ minimization with Markov Chain Monte Carlo sampling to obtain parameter posteriors. Following previous work, we consider how results are affected when $χ^{2}$/N is used for the likelihood function, N being the number of data points, to account for possible correlations in the model and underestimation of the error in the data. Results: We analyze a simple linear model and then move to OMP analysis of elastic angular distributions using a) a 5-parameter model and b) a 6-parameter model. In the linear model, the frequentist and Bayesian approaches yield consistent optima and uncertainty estimates. The same is qualitatively true for the 5-parameter OMP analysis. For the 6-parameter OMP analysis, the parameter posterior is no longer well-approximated by a Gaussian and a covariance-based frequentist prediction becomes unreliable. In all cases, when the Bayesian approach uses $χ^{2}$/N in the likelihood, uncertainties increase by $\sqrt{N}$. Conclusions: When the parameter posterior is near-Gaussian and the same likelihood is used, the frequentist and Bayesian approaches recover consistent parameter uncertainty estimates. If the parameter posterior has significant higher moments, the covariance-only frequentist approach becomes unreliable and the Bayesian approach should be used. Empirical coverage can serve as an important internal check for uncertainty estimation, providing red flags for uncertainty analyses.

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arXiv2023

Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach

We present global predictions of the ground state mass of atomic nuclei based on a novel Machine Learning (ML) algorithm. We combine precision nuclear experimental measurements together with theoretical predictions of unmeasured nuclei. This hybrid data set is used to train a probabilistic neural network. In addition to training on this data, a physics-based loss function is employed to help refine the solutions. The resultant Bayesian averaged predictions have excellent performance compared to the testing set and come with well-quantified uncertainties which are critical for contemporary scientific applications. We assess extrapolations of the model's predictions and estimate the growth of uncertainties in the region far from measurements.

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arXiv2023

The Los Alamos evaluation of $^{239}$Pu neutron-induced reactions in the fast energy range

A major revision of the evaluation of $^{239}$Pu neutron-induced reaction cross sections is reported in the fast energy range. The evaluation starts at 2.5 keV incident neutron energy and has been extended up to 30 MeV. Several other notable changes are included in this evaluation since the release of ENDF/B-VIII.0 including the adoption of the Standards fission cross section, inclusion of new radiative capture data of Mosby et al., inclusion of the (n,2n) data of Meot et al., in addition to advances in the treatment of reaction modeling. In contrast to previous evaluation efforts, this evaluation is reproducible with detailed information stored chronologically utilizing a Git repository. The final evaluation results have been compiled into an ENDF-formatted file, which has been processed successfully through NJOY, checked for internal consistency, benchmarked versus older evaluations and validated against a suite of critical assemblies and pulsed-spheres.

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arXiv2022

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

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 239-Pu 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.

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arXiv2022

Collective enhancement in the exciton model

The pre-equilibrium reaction mechanism is considered in the context of the exciton model. A modification to the one-particle one-hole state density is studied which can be interpreted as a collective enhancement. The magnitude of the collective enhancement is set by simulating the Lawrence Livermore National Laboratory (LLNL) pulsed-spheres neutron-leakage spectra. The impact of the collective enhancement is explored in the context of the highly deformed actinide, 239-Pu. A consequence of this enhancement is the removal of fictitious levels in the Distorted-Wave Born Approximation often used in modern nuclear reaction codes.

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arXiv2022

Horizons: Nuclear Astrophysics in the 2020s and Beyond

Nuclear Astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have opened up with major advances in capabilities across an ever growing number of disciplines and subfields that need to be integrated. We take a holistic view of the field discussing the unique challenges and opportunities in nuclear astrophysics in regards to science, diversity, education, and the interdisciplinarity and breadth of the field. Clearly nuclear astrophysics is a dynamic field with a bright future that is entering a new era of discovery opportunities.

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arXiv2022

Physically Interpretable Machine Learning for nuclear masses

We present a novel approach to modeling the ground state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our Physically Interpretable Machine Learning (PIML) approach incorporates knowledge of physics by using a physically motivated feature space in addition to a soft physics constraint that is implemented as a penalty to the loss function. We train our PIML model on a random set of $\sim$20\% of the Atomic Mass Evaluation (AME) and predict the remaining $\sim$80\%. The success of our methodology is exhibited by the unprecedented $σ_\textrm{RMS}\sim186$ keV match to data for the training set and $σ_\textrm{RMS}\sim316$ keV for the entire AME with $Z \geq 20$. We show that our general methodology can be interpreted using feature importance.

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arXiv2022

Properties of correlated fission fragments from neutron induced fission of Np-237 at incident neutron energies between 200 keV and 100 MeV

Neutron-induced fission of $^{237}$Np has been measured over a wide range of incident neutron energies using a twin Frisch-gridded ionization chamber (TFGIC) and a thin-backed $^{237}$Np target. These measurements were performed at the Los Alamos Neutron Science Center - Weapons Neutron Research (LANSCE - WNR) facility, which provides a collimated beam of neutrons with energies ranging from 100s of keV to 100s of MeV. The data were analyzed using the double-energy ($2E$) method, with mass-dependent corrections for prompt-fission neutrons and pulse height defect. Pre- and post-neutron evaporation average total kinetic energy ($\langle TKE\rangle$) values are reported for 54 incident neutron energies in the energy range $0.20 \le E_n \le 100.0$ MeV and compared to existing data and evaluations. Pre- and post-neutron evaporation mass yields were extracted with a full width at half maximum (FWHM) resolution of 4u and compared to existing data and evaluations. The present $\langle TKE\rangle$ and mass yield data agree with previous results and also with statistical models of $^{237}$Np$(n,f)$ at incident neutron energies between $E_n = 0.2 - 20.0$ MeV. A flattening of the $\langle TKE\rangle$ data is observed (relative to the prediction of the GEF model) above $E_n = 20.0$ MeV. However, the interpretation of this discrepancy is unclear as the analysis method's neglect of incomplete momentum transfer at high energies, as well as pre-equilibrium pre-fission phenomena likely have a significant impact on the measurement at such high incident neutron energies.

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