Nuclear Physics Models Research Articles

The weakly bound states in Gaussian wells: From the binding energy of deuteron to the electronic structure of quantum dots

AbstractGaussian potentials serve as a valuable tool for the comprehensive modeling of short‐range interactions, spanning applications from Nuclear Physics to the artificial confinement of electrons within quantum dots. This study focuses on examining the lowest states within Gaussian wells, with particular emphasis on the weakly bound regime. The analysis delves into the asymptotic behavior of the exact wave function at both small and large distances, motivating the development of a few parametric Ansatz which is locally accurate and yields to a fast convergent basis set. To validate its efficacy, we assess its convergence rate using a toy model of Nuclear Physics, specifically for Deuteron. Furthermore, we employ the expansion of the energy close to the threshold to derive an analytical formula for the binding energy of the Deuteron whose accuracy improves as the effective parameter approaches the critical. To conclude our study, we test the ability of our Ansatz to describe relativistic corrections into a quantum dot and its performance as an orbital in the exploration of the electronic structure of a two‐electron quantum dot.

Baby skyrmion crystals stabilized by vector mesons

In this letter we study soliton crystals in the (2+1)-dimensional analogue model of the (3+1)-dimensional Adkins–Nappi model of nuclear physics. The baby ω-Skyrme model studied here is an O(3) nonlinear σ model coupled to a massive vector meson, the ω-meson. Using recently developed methods in the (3+1)-dimensional ω-Skyrme model we are able to construct soliton crystals in this (2+1)-dimensional baby ω-Skyrme model. The resulting crystals form a hexagonal lattice structure and are qualitatively and quantitatively similar to crystals observed in the standard baby Skyrme model.

The influence of Monte Carlo radiation transport codes and hadron physics models on stray neutron dose estimations in proton therapy: An extended intercomparison

PurposeSelecting a Monte Carlo (MC) radiation transport code and nuclear physics model affects the simulation of secondary neutron fluence energy spectra and the estimation of the neutron dose equivalent in proton therapy. This study compares the MC codes FLUKA, MCNPX and GEANT4 from previous work with two additional MC codes, TOPAS (with either the Binary Intra-nuclear Cascade (BIC) or the Bertini intra-nuclear cascade model (Bert)) and PHITS (with either the Intra-Nuclear Cascade of Liege (INCL) or the Bert model), to facilitate a more comprehensive comparison. Material and methodsSecondary neutron fluence energy spectra were scored at different locations inside a water phantom, exposed to a 10 × 10 cm2 parallel mono-energetic proton beam with energies of 110 MeV, 150 MeV, 180 MeV and 210 MeV. The neutron dose equivalent was estimated by applying fluence-to-dose equivalent conversion coefficients. ResultsDiscrepancies between MC codes and physics models reach 90 % for the total neutron fluence. Differences are most pronounced in the forward direction of the beam due to a notable presence of high-energy neutrons and the higher uncertainty on the neutron cross-section data for these neutrons. Nuclear model selection within PHITS (INCL or Bert) and TOPAS (BIC or Bert) shows a worse agreement for the Bert model than for the other MC codes and nuclear models. The discrepancies in the neutron spectrum simulations propagate into differences in the estimated neutron dose equivalents. They are more prominent for distal positions (up to a factor of 3) than for lateral positions (up to a factor of 2) and decrease at higher proton energies. ConclusionsThis study shows considerable differences in the out-of-field neutron fluence energy spectra calculated by different MC codes and nuclear physics models. Differences in the neutron dose equivalent reach a factor of 3. The discrepancies are most apparent in distal positions and at lower proton energies.

Responses of a 6LiI Bonner Sphere Spectrometer using the Monte Carlo codes PHITS and MCNPX

The response functions (RFs) of a Bonner Sphere Spectrometer (BSS) with a 6LiI thermal neutron detector were calculated using the Monte Carlo codes PHITS (version 3.26) and MCNPX (version 2.7.0), with their own default nuclear data libraries, and physics models. RFs were compared with other published data, obtained for the same spectrometer using the MCNP6.1 code with its own physics models. A discussion on the influence of using different nuclear data libraries and physics models using these codes/versions is analyzed.

The Power of Symmetries in Nuclear Structure and Some of Its Problems

A review of several classical, algebraic models in nuclear structure physics, which use symmetries as an important tool, are presented. After a conceptual introduction to group theory, a selection of models is chosen to illustrate the methods and the power of the usage of symmetries. This enables us to describe very involved systems in a greatly simplified manner. Some problems are also discussed, when ignoring basic principles of nature, such as the Pauli exclusion principle. We also show that occasionally one can rescue these omissions. In a couple of representative models, applications of symmetries are explicitly applied in order to illustrate how extremely complicated systems can be treated. This contribution is meant as a review of the use of algebraic models in nuclear physics, leading to a better understanding of the articles in the same special volume.

Sensitivity Analysis in Coupled Monte Carlo Radiation Transport Simulations

Sensitivity analysis methods have found extensive use in nuclear criticality safety applications for understanding the impact of uncertain nuclear data on eigenvalue estimates. Significant uncertainty exists in nuclear data and nuclear physics models for photon and electron transport applications, and the goal of this work is to explore whether recently developed adjoint-based, first-order generalized perturbation theory reaction rate sensitivity methods can be extended to coupled Monte Carlo radiation transport simulations. This paper presents a rigorous theoretical derivation for this extended sensitivity analysis method, which is then implemented in a one-dimensional test Monte Carlo code. The adjoint-based sensitivity coefficients are found to agree well with reference direct perturbation and deterministic SENSMG sensitivity coefficients for a simple test problem.

On the existence of baby skyrmions stabilized by vector mesons

AbstractIn this paper, we prove the existence of topologically non-trivial solutions of the two-dimensional Adkins–Nappi model of nuclear physics; to this end, we minimize the energy functional by using the classical Skyrme ansatz, as well as a non-radially symmetric generalization of it. In both cases, we show that the minimization procedure preserves the topological degree of the minimization sequence.

Big Bang nucleosynthesis as a probe of new physics

The Big Bang Nucleosynthesis (BBN) model is a cornerstone for the understanding of the evolution of the early universe, making seminal predictions that are in outstanding agreement with the present observation of light element abundances in the universe. Perhaps, the only remaining issue to be solved by theory is the so-called “lithium abundance problem". Dedicated experimental efforts to measure the relevant nuclear cross sections used as input of the model have lead to an increased level of accuracy in the prediction of the light element primordial abundances. The rise of indirect experimental techniques during the preceding few decades has permitted the access of reaction information beyond the limitations of direct measurements. New theoreticaldevelopments have also opened a fertile ground for tests of physics beyond the standard model of atomic,nuclear, statistics, and particle physics. We review the latest contributions of our group for possible solutions of the lithium problem.

Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter.

Dependence of (n,γ)−(γ,n) equilibrium r -process abundances on nuclear physics properties

In most $r$-process expansions, the dominant nuclear evolution occurs in an $(n,\ensuremath{\gamma})\text{\ensuremath{-}}(\ensuremath{\gamma},n)$ equilibrium in which nuclei rapidly exchange neutrons but change charge much more slowly by beta decay. Freeze-out from this equilibrium shapes the final abundances but does not significantly alter the overall global abundance pattern; therefore, it is important to understand the details of $(n,\ensuremath{\gamma})\text{\ensuremath{-}}(\ensuremath{\gamma},n)$ equilibrium both because it is the main evolution phase that determines the final abundance pattern and because it is the starting point for the freeze-out. Through use of a simple but realistic phenomenological nuclear physics model, we show that isotopic abundances versus neutron number in $(n,\ensuremath{\gamma})\text{\ensuremath{-}}(\ensuremath{\gamma},n)$ equilibrium are well approximated as Gaussians. Nuclear pairing causes isotopic abundances to alternate between two Gaussians, and shell effects cause the isotopic abundances to shift from one Gaussian to another when the neutron number crosses a magic number. More complex neutron-separation energy curves versus mass number can be generated by adding a spike function to a linearly declining curve. In such a case, the equilibrium abundance curve jumps from one Gaussian to another for each added spike. Insights from our model can help shed light on how detailed theoretical or experimental nuclear data affect $r$-process nucleosynthesis during the $(n,\ensuremath{\gamma})\text{\ensuremath{-}}(\ensuremath{\gamma},n)$ equilibrium phase.

Comparison of PHITS2.88 and MCNP6.1 for the characterization of a LUPIN-II neutron area monitor

The use of different nuclear data libraries and physics models can be a source of discrepancies in neutron transport simulation. Different Monte Carlo simulation toolkits can be used to characterize neutron monitors, these codes usually employ by default different nuclear data libraries and physics models. This work presents, for the first time, a comparison of MCNP and PHITS for the characterization of a LUPIN-II neutron rem-meter. The most significant discrepancies between the codes have been found around 100 MeV.

Hybrid unstructured mesh geometry model of the next-generation spallation neutron source at LANSCE

We have developed a hybrid Unstructured Mesh (UM)/Constructive Solid Geometry (CSG) model of the next-generation spallation neutron target-moderator-reflector-shield (TMRS) assembly Mark-IV at the Los Alamos Neutron Science Center (LANSCE) to perform MCNP neutronics calculations in support of the engineering design process. The hybrid model consists of an essential component of the new TMRS—the upper target—built as UM and the rest of the model that resembles the original TMRS Mark-III built as CSG. The UM model for the neutronics analysis as well as the model for the subsequent engineering analyses such as structural stress and fluid dynamics are automatically converted from the identical computer aided design (CAD) model. Such an automatic conversion is highly time-efficient and preserves a great level of detail of the original model. In addition, only a relatively small UM portion of our model (0.003 m3) is embedded into the surrounding CSG universe (314 m3), enabling a more efficient MCNP calculation due to the reduced requirements on computer memory and shorter computation time in comparison with a fully extended UM model. Our MCNP calculations focus on the neutronics performance and energy deposition in the upper target. They employ high-energy nuclear physics models, tabular data, and scattering kernels, use an 800 MeV proton beam as a source, tally secondary fast and thermal neutrons with point detectors, and apply mesh tallies and elemental edit outputs to score energy deposition from all particles, i.e., neutrons, photons, protons, and charged pions. Because the use of UM marks a major departure from our original method based entirely on the traditional CSG modeling with MCNPX 2.7.0, we validate the new hybrid UM/CSG model against the legacy CSG model. We found a good agreement between the two models, which demonstrates that MCNP6.2 with UM/CSG hybrids can solve complex neutronics problems at large accelerator facilities, including spallation neutron sources.

A Note on Gauged Baby Skyrmions

In this paper we study a gauged version of the two dimensional Skyrme model of nuclear physics; the field configurations are couples (u, A), where u:{mathbb {R}}^2rightarrow S^2 is a map constant at infinity, which can be classified by its topological degree, and A:{mathbb {R}}^2rightarrow {mathbb {R}}^2 is the gauge field. We prove the existence of rotationally symmetric field configurations which minimize the gauged Skyrme energy on every topological sector (gauged baby skyrmions). Moreover we study the behavior of these skyrmions in the case of weak or strong coupling with the gauge field. In particular, we show that, as observed by many authors by means of numerical simulations, in the strong coupling regime the magnetic flux associated with the gauge field becomes quantized.

Novel event generator for the automated simulation of neutrino scattering

An event generation framework is presented that enables the automatic simulation of events for next-generation neutrino experiments in the Standard Model or extensions thereof. The new generator combines the calculation of the leptonic current based on an automated matrix element generator and the computation of the hadronic current based on a state-of-the-art nuclear physics model. The approach is validated in Standard Model simulations for electron scattering and neutrino scattering. Furthermore, the first fully differential neutrino trident production results are shown in the quasielastic region.

A digital quantum simulation of the Agassi model

A digital quantum simulation of the Agassi model from nuclear physics is proposed and analyzed. The proposal is worked out for the case with four different sites. Numerical simulations and analytical estimations are presented to illustrate the feasibility of this proposal with current technology. The proposed approach is fully scalable to a larger number of sites. The use of a quantum correlation function as a probe to explore the quantum phases by quantum simulating the time dynamics, with no need of computing the ground state, is also studied. Evidence is given showing that the amplitude of the time dynamics of a correlation function in this quantum simulation is linked to the different quantum phases of the system. This approach establishes an avenue for the digital quantum simulation of useful models in nuclear physics.

The neutron star outer crust equation of state: a machine learning approach

Constructing the outer crust of the neutron stars requires the knowledge of the Binding Energy (BE) of the atomic nuclei. Although the BE of a lot of the nuclei is experimentally determined and can be obtained from the AME data table, for the others we need to depend on theoretical models. There exist a lot of physical theories to predict the BE, each with its own strengths and weaknesses. In this paper we apply Machine Learning (ML) algorithms on AME2016 data set to predict the Binding Energy of atomic nuclei. The novel feature of our work is that it is model independent. We do not assume or use any nuclear physics model but use only ML algorithms directly on the AME2016 data set. Our results are further refined by using another ML algorithm to train the errors of the first algorithm, and repeating this process iteratively. Our best algorithm gives σrms ∼ 0.58 MeV for Binding Energy on randomized testing sets. This is comparable to all physics models or ML improved physics models studied in literature till date. Using the predictions of our Machine Learning algorithm, we construct the outer crust equation of state (EoS) of a neutron star and show that our model is comparable to existing models. This work also demonstrates the use of various ML algorithms and a detailed analysis on how we arrived at our best algorithm. It will help the physics community in understanding how to choose an ML algorithm which would be suited for their data set. Our algorithms and best fit model is also made publicly available for the use of the community.

Low energy kaon-nuclei interaction studies at DAΦNE

The aim of the AMADEUS experiment is to investigate the lowenergy antikaon interaction with nucleons and nuclei, exploiting the unique lowmomentum beam of kaons produced by the DAΦNE collider at LNF-INFN, to constrain hadronic nuclear physics models in the strangeness -1 sector. As a first step the data collected in 2004/2005 by the KLOE collaboration, consisting in a complex of K−absorptions in H,4He,9Be and12C, was analyzed, leading to the first invariant mass spectroscopic study with very low momentum (about 100 MeV) in-flight K−captures. A dedicated pure Carbon target was also implemented in the central region of the KLOE detector, providing a high statistic reference sample of pure at-rest K−nuclear interaction. The first measurement of the non-resonant transition amplitude $\left| {{T_{{K^ - }n \to \Lambda {\pi ^ - }}}} \right|$ at $\sqrt s = 33$ MeV below the ${\text{\bar KN}}$ threshold is presented, in relation with the Λ(1405) properties studies.

High-level fuel fabrication facility designs from discrete-event simulation

Like other industrial processes, the production of metallic nuclear fuels (MNF) requires that fabrication facilities be able to reliably meet production demands, operate efficiently, and adhere to federal and local safety regulations. In turn, the set of design variables employed by such a facility, such as operations policies, infrastructure and machinery purchased, and the type and number of staff hired, directly impact a facility’s ability to satisfy these goals. Therefore, facility designers must carefully determine which set of design variable values optimally satisfies these constraints. In this paper, we explore how values for these high-level design variables, namely hiring requirements, can be determined in the context of nuclear fuel manufacturing through the coupling of physics-based and discrete-event simulation technologies. Using the Versatile Test Reactor (VTR) program as a case study, we demonstrate how SCALE and MCNP nuclear physics model outputs can be integrated into ExtendSim discrete-event simulation (DES) models of the fuel fabrication process to determine the optimal number of staff hired to ensure fuel production goals are met, operations comply with effective dose limit regulations, and overall project costs are reduced.

Quantum computing for the Lipkin model with unitary coupled cluster and structure learning ansatz * *A.C. appreciates the financial support of Advanced Leading Graduate Course for Photon Science, the University of Tokyo. H.L. acknowledges the JSPS Grant-in-Aid for Early-Career Scientists (18K13549) and the JSPS Grant-in-Aid for Scientific Research (S) (20H05648)

We report a benchmark calculation for the Lipkin model in nuclear physics with a variational quantum eigensolver in quantum computing. Special attention is paid to the unitary coupled cluster (UCC) ansatz and structure learning (SL) ansatz for the trial wave function. Calculations with both the UCC and SL ansatz can reproduce the ground-state energy well; however, it is found that the calculation with the SL ansatz performs better than that with the UCC ansatz, and the SL ansatz has even fewer quantum gates than the UCC ansatz.

Parameterizing qudit states

Quantum systems with a finite number of states at all times have been a primary element of many physical models in nuclear and elementary particle physics, as well as in condensed matter physics. Today, however, due to a practical demand in the area of developing quantum technologies, a whole set of novel tasks for improving our understanding of the structure of finite-dimensional quantum systems has appeared. In the present article we will concentrate on one aspect of such studies related to the problem of explicit parameterization of state space of an NN-level quantum system. More precisely, we will discuss the problem of a practical description of the unitary SU(N){SU(N)}-invariant counterpart of the NN-level state space BN{\mathcal{B}_N}, i.e., the unitary orbit space BN/SU(N){B_N/SU(N)}. It will be demonstrated that the combination of well-known methods of the polynomial invariant theory and convex geometry provides useful parameterization for the elements of BN/SU(N){B_N/SU(N)}. To illustrate the general situation, a detailed description ofBN/SU(N){B_N/SU(N)} for low-level systems: qubit (N=2{N= 2}), qutrit (N=3{N=3}), quatrit (N=4{N= 4}) - will be given.