Progress and simulations for intranuclear neutron-antineutron transformations in Ar 18 40
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39 ≤ A ≤ 58↗
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Results for “Models and methods for nuclear reactions”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Ab initio leading order effective potential for elastic proton scattering based on the symmetry-adapted no-core shell model
Calculating microscopic optical potentials for elastic scattering at intermediate energies from light nuclei in an ab initio fashion within the Watson expansion has been established within the last few years. Based on the Watson expansion of the multiple scattering series, we employ a nonlocal translationally invariant nuclear density derived within the symmetry-adapted no-core shell model (SA-NCSM) framework from a chiral next-to-next-to-leading order (NNLO) nucleon-nucleon interaction and the very same interaction for a consistent full-folding calculation of the effective (optical) potential for nucleon-nucleus scattering for medium-heavy nuclei. The leading order effective (optical) folding potential is computed by integrating over a translationally invariant SA-NCSM one-body scalar density, spin-projected momentum distribution, and the Wolfenstein amplitudes 𝐴, 𝐶, and 𝑀. The resulting nonlocal potentials serve as input for a momentum space Lippmann-Schwinger equation. In the SA-NCSM, the model space is systematically up-selected using Sp(3,ℝ) symmetry considerations. For the light nucleus of 6 He, we establish a systematic selection scheme in the SA-NCSM for scattering observables. Then, we apply this scheme to calculations of scattering observables, such as differential cross sections, analyzing powers, and spin rotation functions for elastic proton scattering from 20 Ne and 40 Ca in the energy regime between 65 and 200 MeV, and compare to available data. Furthermore, our calculations show that the leading order effective nucleon-nucleus potential in the Watson expansion of multiple scattering theory obtained from an up-selected SA-NCSM model space describes 40 Ca elastic scattering observables reasonably well to about 60 degrees in the center-of-mass frame, which coincides roughly with the validity of the NNLO chiral interaction used to calculate both the nucleon-nucleon amplitudes and the one-body scalar and spin nuclear densities.
Ab initio calculations↗
Isotopically resolved neutron total cross sections at intermediate energies
The neutron total cross sections σ tot of 16,18 O, 58 64 Ni, 103 Rh, and 112,124 Sn have been measured at the Los Alamos Neutron Science Center from low to intermediate energies (3 ≤ E lab ≤ 450 MeV) by leveraging wave-form-digitizer technology. The σ tot relative differences between isotopes are presented, revealing additional information about the isovector components needed for an accurate optical-model description away from stability. Digitizer-enabled σ tot -measurement techniques are discussed and a series of uncertainty-quantified dispersive optical model (DOM) analyses using these new data is presented, validating the use of the DOM for modeling light systems ( 16,18 O) and systems with open neutron shells ( 58,64 Ni and 112,124 Sn). Here, the valence-nucleon spectroscopic factors extracted for each isotope reaffirm the usefulness of high-energy proton reaction cross sections for characterizing depletion from the mean-field expectation.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Uncertainty-quantified phenomenological optical potentials for single-nucleon scattering
Optical-model potentials (OMPs) continue to play a key role in nuclear reaction calculations. However, the uncertainty of phenomenological OMPs in widespread use—inherent to any parametric model trained on data—has not been fully characterized, and its impact on downstream users of OMPs remains unclear. Here we assign well-calibrated uncertainties for two representative global OMPs, those of Koning-Delaroche and Chapel Hill '89, using Markov-chain Monte Carlo for parameter inference. By comparing the canonical versions of these OMPs against the experimental data originally used to constrain them, we show how a lack of outlier rejection and a systematic underestimation of experimental uncertainties contributes to bias of, and overconfidence in, best-fit parameter values. Our updated, uncertainty-quantified versions of these OMPs address these issues and yield complete covariance information for potential parameters. Scattering predictions generated from our ensembles show improved performance both against the original training corpora of experimental data and against a new “test” corpus comprising many of the experimental single-nucleon scattering data collected over the last twenty years. Finally, we apply our uncertainty-quantified OMPs to two case studies of application-relevant cross sections. We conclude that, for many common applications of OMPs, including OMP uncertainty should become standard practice. Furthermore, to facilitate their immediate use, digital versions of our updated OMPs and related tools for forward uncertainty propagation are included as Supplemental Material.
150 ≤ A ≤ 189↗
Collective enhancement in the exciton model
The preequilibrium 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 estimated by simulating the Lawrence Livermore National Laboratory (LLNL) pulsed-spheres neutron-leakage spectra. Here 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.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
α -cluster microscopic study of C 12 + C 12 fusion toward the zero energy limit
The carbon burning process is a fundamental step of stellar evolution and governs the synthesis of chemical elements important for the formation of life. In this work, we utilize the microscopic hybrid α cluster (HαC) model and an analytical approach, both in the framework of the Imaginary Time Method (ITM), to study the carbon fusion reaction towards zero energy. We obtain the values of the cross sections, astrophysical factors and correlate our results to collective motion. We also include a calculation for the 2 + carbon fusion and discuss a possible experimental investigation. Our results confirm direct experimental and theoretical results close to the barrier, while suggest possible 2 + mixtures in the indirect experimental data. Furthermore, our study offers an accurate view of the burning process in the somewhat unexplored low energy region.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Quantum physics of stars
Stars are slowly developing objects; the lifetimes of the different burning phases are determined by the strength of nuclear reactions, which in turn are defined by the quantum structure of the associated nuclei at the threshold and the respective reaction mechanisms. Stars, from the nuclear physics perspective, are cold environments where only a few of the key nuclear reactions have been measured at the actual stellar plasma temperatures. This is also the case for more dynamic astrophysical phenomena from the big bang to stellar explosions. Most of the nuclear reaction rates are therefore based on theoretical extrapolations. A number of discrepancies between these predictions and the associated stellar signatures have been observed, and many may be due to low-energy or near-threshold quantum effects. These effects need to be understood in order to reliably model nuclear reaction processes, not only for stars but also for low-temperature plasma environments such as controlled magnetic or inertial confinement fusion systems, which operate in similar temperature regimes. This review summarizes the various theoretical techniques presently used for deriving reaction rates and discusses possible quantum effects that may impact the reaction cross section near the reaction threshold. These resemble enhanced single-particle and cluster structures near threshold and associated interference effects. New experimental techniques such as deep-underground accelerators or the study of transfer reactions to mimic the quantum-mechanical transition strength, the so-called Trojan horse method, provide ways to directly or indirectly probe the reaction features that determine the reaction rates at stellar energies. Furthermore, this is demonstrated on a number of key nuclear reactions for different nucleosynthesis environments. Finally, current inconsistencies between experimental predictions and observations are discussed.
Models & methods for nuclear reactions↗
Nuclear level density and γ -decay strength of Sr 93
This work presents the first experimentally determined nuclear level density and γ-ray strength function of the short-lived fission product 93 Sr, accomplished using the β-Oslo method. Direct measurement of the 92 Sr(n, γ) 93 Sr cross section is not currently possible, as the half-life of 2.66 hours is too short; instead, 93 Sr was formed through β decay of 93 Rb to excitation energies around the neutron separation energy. The γ-ray spectra were measured using a total absorption spectrometer at the National Superconducting Cyclotron Laboratory (NSCL) at Michigan State University (MSU). The statistical properties of the 93 Sr nucleus were experimentally determined, including the γ-ray strength function and nuclear level density. At low energies, the γ-ray strength function exhibits a constant γ-decay strength, rather than a slightly increasing strength with decreasing γ-ray energy as had been previously observed for several nuclei in this mid-mass region. Finally, these statistical properties were then implemented in the reaction code TALYS1.95 to calculate the 92 Sr(n, γ) 93 Sr cross section.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Impact of tensor forces on quasifission product yield distributions
Quantum shell effects are crucial for the stability and structure of atomic nuclei and play a key role in the discovery of superheavy elements. Furthermore, during nuclear collisions, the dynamical evolution of these shell effects disrupts the equilibration process necessary for forming a compound nucleus, leading to the breakup of the initial composite as a result of quasifission. As such, quasifission reactions hinder the formation of a superheavy element.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Production and discovery of neutron-rich isotopes by fragmentation of 198 Pt
Production cross sections were measured for fragments produced by an 85 MeV/u 198 Pt beam incident on a beryllium target. Event-by-event particle identification of A, Z, and q for the reaction products was performed by employing energy loss, time-of-flight, magnetic rigidity, and total kinetic energy measurements. Over 70 nuclei in the Hf-Pt region were identified, including three isotopes first observed in this work: 191,192 Hf and 189 Lu. Due to the existence of multiple charge states between H-like and C-like ions, a new analysis method was introduced, incorporating Monte Carlo calculations of charge state fractions for a given charge state of the projectile residue just after the reaction. For the first time, charge-state probability distribution functions after the reaction have been deduced from experimental data. Furthermore, this study provides insight into how to produce key nuclides near N = 126 and the ability of a fragmentation residue to retain electrons from the primary beam.
190 ≤ A ≤ 219↗
Description of the multinucleon transfer mechanism for Ca 48 + Pu 244 and Kr 86 + Pt 198 reactions in a quantal transport approach
Multinucleon transfer (MNT) reactions involving heavy projectile and target combinations stand as a promising method for synthesizing new neutron-rich exotic nuclei, which may not be possible using hot or cold fusion reactions or fragmentation. Exploring the mechanisms behind MNT reactions is essential and it requires a comprehensive theoretical framework that can explain the physical observables in these reactions. This work aims to show that the quantal diffusion approach based on the stochastic mean-field (SMF) theory is capable of explaining the reaction dynamics observed in MNT reactions. Primary product mass distributions in 48 Ca + 244 Pu reaction at E c.m. = 203.2 MeV and 86 Kr + 198 Pt reaction at E c.m. = 324.2 MeV are calculated and compared with the available experimental data. In this work, we utilize the time-dependent Hartree-Fock (TDHF) calculations to analyze the mean-field reaction dynamics computationally in the reactions 48 Ca + 244 Pu and 86 Kr + 198 Pt for a broad range of initial angular momenta. Quantal transport description based on the SMF approach is used to calculate quantal diffusion coefficients and mass variances in 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary products arising from quasifission reactions are described by joint probability distribution in the SMF approach and those arising from fusion-fission are estimated by using the statistical deexcitation code gemini + +. Mean values of charge and mass numbers, scattering angles of the primary reaction products, and the total kinetic energies after the collision are calculated within the TDHF framework for a broad range of initial angular momenta. Throughout all the collisions, drift toward the mass symmetry and large mass dispersion associated with this drift are observed. Here, the calculated primary fragment and mass distributions using the SMF approach successfully explain experimental observations for the 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary mass distributions, mean values of binary products, and mass dispersions are determined and results are compared with the available experimental data. The observed agreement between the experimental data and SMF results highlights the effectiveness of the quantal diffusion mechanism based on the SMF approach, which does not include any adjustable parameters other than standard parameters of Skyrme energy density functional.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Fast-neutron-induced fission of Pu 240 and Pu 242
Herein we report the measurement of the total kinetic energy (TKE) release in the fast neutron induced fission of 240 Pu and 242 Pu. The results are compared to the predictions of the GEF model, the CGMF model, and the model of Denisov and Sedykh as well as previous exptl. work on these reactions. Our absolute measurements of the TKE release are in good agreement with the previous measurements of Nethaway et al. for the interaction of 14.8 MeV neutrons with 240 Pu [Phys. Rev. C16, 1907 (1977)] and of Winkelmann and Aumann for the interaction of 15 MeV neutrons with 242 Pu [Phys. Rev. C30, 934 (1984)]. The general trends of the measured TKE values agree with phenomenol. models but the variances of the TKE distributions are significantly less than predicted by various models. The mean postneutron emission TKE release decreases nonlinearly with increasing neutron energy and can be represented as TKE(MeV) = 175.8 ± 0.3 - (2.4 ± 0.8) lo g 10 E n - (1.4 ± 0.4) lo g 10 $E^{2}_{n}$ for 240 Pu and TKE(MeV) = 177.1 ± 0.3 - (1.2 ± 0.9) lo g 10 E n - (1.8 ± 0.5)lo g 10 $E_{n}^{2}$ for 242 Pu.
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Learning nuclear cross sections across the chart of nuclides with graph neural networks
We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.
Machine learning↗
Constraining level densities through quantitative correlations with cross-section data
The adopted level densities (LD) for the nuclei produced through different reaction mechanisms significantly impact the accurate calculation of cross sections for the different reaction channels. Many common LD models make simplified assumptions regarding the overall behavior of the total LD and the intrinsic spin and parity distributions of the excited states. However, very few experimental constraints are taken into account in these models: LD at neutron separation energy coming from average spacings of s- and p-wave resonances ( D 0 and D 1 , respectively) whenever they have been previously measured, and the sometimes subjective extrapolation of discrete levels. These, however, constrain the LD only in very specific regions of excitation energy, and for specific spins and parities. This work aims to establish additional experimental constraints on LD through quantitative correlations between cross sections and LD. This allows for fitting and the determination of detailed structures in LD. For this we use the microscopic Hartree-Fock-Bogoliubov (HFB) LD as a starting point as the HFB LD provide a more realistic spin and parity distributions than phenomenological models such as Gilbert-Cameron (GC). We then associate variations predicted by the HFB model with the structure observed in double-differential cross sections at low outgoing neutron energy, a region that is dominated by the LD input. We also use (n, p) on 56 Fe , as an example case where angle-integrated cross sections are extremely sensitive to LD. For comparison purposes we also perform calculations with the GC model. With this approach we are able to perform fits of the LD based on actual experimental data, constraining the model and ensuring its consistency. This approach can be particularly useful in extrapolating the LD to nuclei for which high-excited discrete levels and/or values of D 0 or D 1 are unknown. Finally, it also predicts neutron-induced inelastic γ cross sections that in some cases can differ significantly from more standard phenomenological LD models such as GC.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics
In this paper, we describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Kinetic-energy dissipation and fluctuations in strongly damped heavy-ion collisions within the stochastic mean-field approach
Microscopic mean-field approaches have been successful in describing the most probable reaction outcomes in low-energy heavy-ion reactions. However, those approaches are known to severely underestimate dispersions of observables around the average values that has limited their applicability. Recently it has been shown that a quantal transport approach based on the stochastic mean-field (SMF) theory significantly improves the description, while its application has been limited so far to fragment mass and charge dispersions. In this work, we extend the quantal transport approach based on the SMF theory for relative kinetic energy dissipation and angular momentum transfer in low-energy heavy-ion reactions. Based on the SMF concept, analytical expressions are derived for the radial and tangential friction and associated diffusion coefficients. Those quantal transport coefficients are calculated microscopically in terms of single-particle orbitals within the time-dependent Hartree-Fock (TDHF) approach. As the first application of the proposed formalism, we consider the radial linear momentum dispersion, neglecting the coupling between radial and angular momenta. We analyze the total kinetic energy (TKE) distribution of binary reaction products in the 136 Xe + 208 Pb reaction at E c.m. =526 MeV and compare with experimental data. From time evolution of single-particle orbitals in TDHF, the radial diffusion coefficient is computed on a microscopic basis, while a phenomenological treatment is introduced for the radial friction coefficient. By solving the quantal diffusion equation for the radial linear momentum, the dispersion of the radial linear momentum is obtained, from which one can construct the TKE distribution. We find that the calculations provide a good description of the TKE distribution for strongly damped events with large energy losses, TKEL ≳ 150 MeV. However, the calculations underestimate the TKE distribution for smaller energy losses. Further studies are needed to improve the technical details of calculations.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Extension of the Hauser-Feshbach fission fragment decay model to multichance fission
The Hauser-Feshbach fission fragment decay model, HF 3 D, which calculates the statistical decay of fission fragments, has been expanded to include multichance fission, up to neutron incident energies of 20 MeV. The deterministic decay takes as input prescission quantities—fission probabilities and the average energy causing fission—and postscission quantities—yields in mass, charge, total kinetic energy, spin, and parity. From these fission fragment initial conditions, the full decay is followed through both prompt and delayed particle emissions, allowing for the calculation of prompt neutron and γ properties, such as multiplicity and energy distributions, both independent and cumulative fission yields, and delayed neutron observables. Here, we describe the implementation of multichance fission in the HF 3 D model, and show an example of prompt and delayed quantities beyond first-chance fission, using the example of neutron-induced fission on 235 U. This expansion represents significant progress in consistently modeling the emission of prompt and delayed particles from fissile systems.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Informing nuclear physics via machine learning methods with differential and integral experiments
Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.
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