Applications In Nuclear Physics Research Articles

Some educational uses of EDXRF for technologists in health science

Abstract Physicists teach physics to students of various academic disciplines such as physics, chemistry, health sciences, biology, geology, environmental sciences, etc. Energy dispersive X-ray fluorescence spectroscopy (EDXRF) is a case of application of atomic physics in the real world. Students, through applications of EDXRF, may find out how the basic concepts of atomic physics can be used for elemental analysis of samples of their scientific interest and everyday life. This paper presents some educational implementations of EDXRF and highlights the importance of incorporating this spectroscopic method in undergraduate or postgraduate labs or lectures in the field of health sciences. Five experimental activities, a general one for understanding X-ray spectra and the others for the analysis of substances/materials related to chemistry/toxicology, dentistry, optics, and cosmetics, are presented and discussed.

Emerging New-Generation Semiconductor Single Crystals of Metal Halide Perovskites for Radiation Detection

Radiation detection uses semiconductor materials to convert high-energy photons into charge (direct detection) or low-energy photons (indirect detection), and it has a wide range of applications in nuclear physics, medical imaging, astronomical detection, homeland security, and other fields. Metal halide perovskites have the advantages of high frequency number, high carrier mobility, high defect tolerance, low defect density, adjustable band gap, and fast light response, and they have wide application prospects in the field of radiation detection. However, the research is still in its infancy stage, and it is far from meeting the requirements of industrial application. This paper focuses on the advantages of metal halide perovskite single-crystal materials in both semiconductors-based direct conversion detection and scintillator-based indirect detection as well as the latest progress in this promising field. This paper not only introduces the latest application of lead halide perovskite monocrystalline materials in high-energy electromagnetic radiation detection (X-ray and γ-rays), but it also introduces the latest development of α-particle/β-particle/neutron detection. Finally, this paper points out the challenges and future prospects of metal halide perovskite single-crystal materials in radiation detection.

Generalized bivariate Kummer-beta distribution with marginals defined on the unit interval.

In this paper, a generalized bivariate Kummer-beta distribution is proposed. The name derives from the fact that its particular cases include univariate Kummer-beta distributions. This distribution generalizes a number of existing bivariate beta distributions, including Nadarajah's bivariate distributions, Libby and Novick's bivariate beta distribution and a central bivariate Kummer-beta distribution. Various properties associated with this newly introduced distribution are derived. The derived properties include product moments, marginal densities, marginal moments, conditional densities, conditional moments, Rényi entropy and Shannon entropy. Motivated by possible applications in economics, genetics, hydrology, meteorology, nuclear physics, and reliability, we also derive distributions of the product and the ratio of the components following the proposed distribution. Parameter estimation by maximum likelihood method is discussed by deriving expressions for score functions. Inference based on maximum likelihood estimation supposes that the maximum likelihood estimators have zero bias and zero mean squared errors. A simulation study is performed to check this for finite samples.

Neural-network approach to running high-precision atomic computations

Modern applications of atomic physics, including the determination of frequency standards and the analysis of astrophysical spectra, require prediction of atomic properties with exquisite accuracy. For complex atomic systems, high-precision calculations are a major challenge due to the exponential scaling of the involved electronic configuration sets. This exacerbates the problem of required computational resources for these computations and makes indispensable the development of approaches to select the most important configurations out of otherwise intractably huge sets. We have developed a neural-network (NN) tool for running high-precision atomic configuration interaction (CI) computations with iterative selection of the most important configurations. Integrated with the established atomic codes, our approach results in computations with significantly reduced computational requirements in comparison with those without NN support. We showcase a number of NN-supported computations for the energy levels of Fe16+ and Ni12+ and demonstrate that our approach can be reliably used and automated for solving specific computational problems for a wide variety of systems. Published by the American Physical Society 2024

Investigation of thermal properties of Hulthén potential from statistical and superstatistical perspectives with various distributions

The Hulthén potential is a short-range potential widely used in various fields of physics. In this paper, we investigate the distribution functions for the Hulthén potential by using statistical and superstatistical methods. We first review the ordinary statistics and superstatistics methods. We then consider distribution functions, such as uniform, 2-level, gamma, and log-normal and F distributions. Finally, we investigate the behavior of the Hulthén potential for statistical and superstatistical methods and compare the results with each other. We use the Tsallis statistics of the superstatistical system. We conclude that the Tsallis behavior of different distribution functions for the Hulthén potential exhibits better results than the statistical method. We examined the thermal properties of the Hulthén potential for five different distributions: Uniform, 2-level, Gamma, Log-normal, and F. We plotted the Helmholtz free energy and the entropy as functions of temperature for various values of q. It shows that the two uniform and 2-level distributions have the same results due to the universal relationship and that the F distribution does not become ordinary statistics at q = 1. It also reveals that the curves of the Helmholtz free energy and the entropy change their order and behavior as q increases and that some distributions disappear or coincide at certain values of q. One can discuss the physical implications of our results and their applications in nuclear and atomic physics in the future.

SYNTHESIS STRUCTURAL AND MAGNETIC PROPERTIES OF TITANIUM DOPED MAGNESIUM ZINC FERRITE

Magnetic ceramics or ferrites are very well established group of magnetic materials which exhibits ferrimagnetism. It possess high electrical resistivity, low eddy current and dielectric loss which makes it superior than the other ordinary magnetic oxides that contain the ferric oxide (Fe₂O₃) as their basic magnetic compounds. The ceramic magnets are widely used in high frequency transformer application. [(Mg, Mn, F12o4)] spinels are used in square-loop application. Ceramic materials found application in nuclear physics. In nuclear reactors ceramics are used as core components. The studies include the preparation and analysis of Titanium doped Magnesium zinc ferrite. Here, Titanium doped magnesium zinc ferrite nanoparticles (NPS) were synthesized using solid state reaction method. Titanium doping can enhance the magnetic properties of magnesium zinc ferrite in several ways these can contribute to improved magnetic properties , such as increased magnetic moment, saturation magnetization, and magnetic ordering , making titanium – doped magnesium zinc ferrite a promising material for various applications including magnetic storage, sensors, and spintronics. According to the stoichiometric equation [Mg0.9Zn 0.1TixFe2-xO4] at different values of x, (x=0.1, 0.3) are synthesised. Calcination and sintering of the samples is made by high temperature PID controlled muffle furnace which can go up to 1000°C. The structural and magnetic properties are examined using XRD, FTIR and Curie temperature analysis. From the XRD analysis it revels the phase structure and lattice parameters of the samples. XRD analysis gives maximum intensity of (h k l) values as (3 1 1) in both cases. It also reveals that no secondary phases are produced during growth. That means the specimens FCC stacking. FTIR analysis of the samples done using Fourier transformer infrared spectroscopy. From this analysis it revels the detailed atomic structure and bonding of the samples. The bands obtained reveal spinal ferrite structure. Curie temperature of the specimens is determined by Curie temperature apparatus for the study of magnetic properties of the specimens.

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This article examines the features of the use of nuclear physics in agriculture as an important tool for improving production processes, increasing yields and quality of agricultural products, as well as for ensuring sustainable development of the agricultural sector. The application of nuclear physics in agriculture requires international cooperation and the exchange of experience between States, which contributes to the accelerated implementation of innovative solutions and technologies. The use of nuclear physics in agriculture in general has great potential to improve the living conditions of rural residents, ensure food security and sustainable development of the agricultural sector. In the context of continuous population growth, climate change and limited natural resources, agricultural producers are forced to seek innovative methods to increase the yield and quality of agricultural products.

Stability analysis and soliton solutions of the (1+1)-dimensional nonlinear chiral Schrödinger equation in nuclear physics

The nonlinear Schrödinger equation equation is one of the most important physical models used in optical fiber theory to explain the transmission of an optical soliton. The field of chiral soliton propagation in nuclear physics is very interesting because of its numerous applications in communications and ultra-fast signal routing systems. The (1+1)-dimensional chiral dynamical structure that describes the soliton behaviour in data transmission is dealt with in this work using a variety of in-depth analytical techniques. This work has applications in particle physics, ionised science, nuclear physics, optics, and other applied mathematical sciences. We are able to develop a variety of solutions to demonstrate the behaviour of solitary wave structures, periodic soliton solutions, chiral soliton solutions, and bell-shaped soliton solutions with the use of applied techniques. Moreover, in order to verify the scientific calculations, the stability analysis for the observed solutions of the governing model is taken into consideration. In addition, the 3-dimensional, contour, and 2-dimensional visuals are supplied for a better understanding of the behaviour of the solutions. The employed strategies are dependable, uncomplicated, and effective; yet have not been utilised with the governing model in the literature that is now accessible. The resulting outcomes have impressive applications across a large number of study areas and computational physics phenomena representing real-world scenarios. The methods applied in this model are not utilized on the given models in previous literature so we can say that these describe the novelty of the work.

Is infrared-collinear safe information all you need for jet classification?

Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are tractable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius and a cut on the subjet’s transverse momenta serve as tunable hyperparameters enabling a controllable sensitivity to soft emissions and nonperturbative effects. We demonstrate the performance of JFNs for quark vs. gluon and Z vs. QCD jet tagging. For small subjet radii and transverse momentum cuts, the performance of JFNs is equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that infrared-collinear unsafe information is not necessary to achieve strong discrimination for both cases. As the subjet radius is increased, the performance of the JFNs remains essentially unchanged until physical thresholds that we identify are crossed. For relatively large subjet radii, we show that the JFNs may offer an increased model independence with a modest tradeoff in performance compared to classifiers that use the full particle information of the jet. These results shed new light on how machines learn patterns in high-energy physics data.

Investigating neutron scattering in a Spherical Proportional Counter: A tabletop experiment

In this paper, we report on a tabletop experiment studying neutron scattering in a Spherical Proportional Counter using an Am-Be source. Systematic studies were carried out to investigate the effect of gas mixture, pressure, operating voltage, and sphere size on the drift time-rise time relationship of the signal in a spherical proportional counter. Our experimental results showed good agreement with MagBoltz-based simulations. These findings are a crucial step towards measuring the quenching factor in gases using a neutron beam for the New Experiments With Spheres-Gas (NEWS-G) experiment and has important implications for the development of neutron detection techniques and their potential applications in nuclear and particle physics.

Water-based quantum dots liquid scintillator for particle physics

Liquid scintillators are typically composed from organic compounds dissolved in organic solvents. However, usage of such material is often restricted due to fire safety and environmental reasons. Because of this, R&D of water-based liquid scintillators is of extreme relevance; yet, no such scintillators have been made commercially available as yet. Here, we investigate an alternative, water-based quantum dots liquid scintillator. Pre-determined and controllable optical properties of the quantum dots, as well as the existence of large libraries of established protocols for their dispersion in aqueous solutions, make them an attractive option for nuclear and particle physics applications. We characterize the optical properties of water-based quantum dots liquid scintillator and find that most of its optical properties are preserved upon quantum dots' phase transfer into water, through the addition of an oleic acid hydrophilic layer. Using the developed scintillator, the time and charge responses from atmospheric muons are measured, highlighting the practical viability of water-based quantum dots liquid scintillators for nuclear and particle physics, special interest on neutrino physics.

Dense Polarized Positrons from Beam-Solid Interaction.

Relativistic positron sources with high spin polarization have important applications in nuclear and particle physics and many frontier fields. However, it is challenging to produce dense polarized positrons. Here we present a simple and effective method to achieve such a positron source by directly impinging a relativistic high-density electron beam on the surface of a solid target. During the interaction, a strong return current of plasma electrons is induced and subsequently asymmetric quasistatic magnetic fields as high as megatesla are generated along the target surface. This gives rise to strong radiative spin flips and multiphoton processes, thus leading to efficient generation of copious polarized positrons. With three-dimensional particle-in-cell simulations, we demonstrate the production of a dense highly polarized multi-GeV positron beam with an average spin polarization above 40% and nC-scale charge per shot. This offers a novel route for the studies of laserless strong-field quantum electrodynamics physics and for the development of high-energy polarized positron sources.

Theories of Relativistic Dissipative Fluid Dynamics.

Relativistic dissipative fluid dynamics finds widespread applications in high-energy nuclear physics and astrophysics. However, formulating a causal and stable theory of relativistic dissipative fluid dynamics is far from trivial; efforts to accomplish this reach back more than 50 years. In this review, we give an overview of the field and attempt a comparative assessment of (at least most of) the theories for relativistic dissipative fluid dynamics proposed until today and used in applications.

Attosecond science the art of making electron movies

After 22 years since the first experimental demonstration of attosecond pulses, the attosecond science has become a very active field of research, with prominent applications in atomic, molecular and solid-state physics. Experimental advances, in terms of new sources, devices and techniques, are still required, together with new theoretical tools and approaches, but attosecond physics has firmly established as a mature research field.

Applications of nuclear physics to energy: the INFN-E project of the National Institute of Nuclear Physics (INFN)

In this talk, the main issues in radioactive waste management worldwide will be briefly introduced, as well as the world roadmap towards nuclear fusion. Then, an overview of the activities of the INFN-E (INFN Energia) strategic project will be given, showing how INFN's skills on basic theoretical aspects and on the design, construction and use of accelerators and radiation detectors can be applied to specific projects for the characterization and monitoring of radioactive waste as a means to increase nuclear safety and in the context of advanced plants for the study of nuclear fusion.

Electron plasma diagnostics in ELTRAP by electron cyclotron resonance heating method.

Electron cyclotron resonance heating method of Particle-in-Cell code was used to analyze heating phenomena, axial kinetic energy, and self-consistent electric field of confined electron plasma in ELTRAP device by hydrogen and helium background gases. The electromagnetic simulations were performed at a constant power of 3.8 V for different RF drives (0.5 GHz- 8 GHz), as well as for 1 GHz constant frequency at these varying amplitudes (1 V-3.8 V). The impacts of axial and radial temperatures were found maximum at 1.8 V and 5 GHz as compared to other amplitudes and frequencies for both background gases. These effects are higher at varying radio frequencies due to more ionization and secondary electrons production and maximum recorded radial temperature for hydrogen background gas was 170.41 eV. The axial kinetic energy impacts were found more effective in the outer radial part (between 0.03 and 0.04 meters) of the ELTRAP device due to applied VRF through C8 electrode. The self-consistent electric field was found higher for helium background gas at 5 GHz RF than other amplitudes and radio frequencies. The excitation and ionization rates were found to be higher along the radial direction (r-axis) than the axial direction (z-axis) in helium background gas as compared to hydrogen background gas. The current studies are advantageous for nuclear physics applications, beam physics, microelectronics, coherent radiation devices and also in magnetrons.

In‐Source High‐Resolution Spectroscopy Using an Integrated Tunable Raman Laser

AbstractTunable single‐frequency lasers are the most prominent tool for high‐resolution spectroscopy, allowing for the study and exploitation of the electronic structure of atoms. A significant milestone relies on the demonstration of integrated laser technology for performing such a task. The device presented here is composed of a compact Fabry–Perot monolithic resonator capable of producing tunable and Fourier‐limited nanosecond pulses with a MHz‐class frequency stability without active cavity stabilization elements. It also has the remarkable capability of exploiting the Raman effect to funnel efficiently the broad spectrum of an input laser to a spectrally‐bright Stokes pulse at hard‐to‐access wavelength ranges. The targeted atom for the demonstrations is 152Sm, released as an atomic vapor in a hot cavity environment. Here, the Stokes field is tuned to a wavelength of 433.9 nm, while a crossed‐beams spectroscopy setup is used to minimize the Doppler broadened spectral features of the atoms. With this work, the suitability of integrated diamond Raman lasers as a high‐resolution in‐source spectroscopy tool is demonstrated, enabling many applications in atomic and nuclear physics. The integrated form‐factor and inherent simplicity makes such a laser an interesting prospect for quantum‐technology based sensing systems and related applications.

Model Mixing Using Bayesian Additive Regression Trees

In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible nonparametric model that learns the local strengths and weaknesses of each simulator. This article proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. Supplementary materials for this article are available online. Source code is available at https://github.com/jcyannotty/OpenBT.

Accurate simultaneous lead stopping power and charge-state measurements in gases and solids: Benchmark data for basic atomic theory and nuclear applications

We have measured for the first time simultaneously both the mean charge states and stopping powers of (35–280) MeV/u 208Pb ions in gases and solids with an accuracy of 1%. The existence at lower energies and disappearance at higher of density effects in the charge-state distribution and the corresponding stopping power are directly confirmed and comparisons with widely used theories and simulations for heavy ions demonstrate strong deviations of up to 27%. However, an unprecedented prediction power of better than 3% has been achieved for the energy loss when the measured mean charge-states are implemented in the Lindhard-Sørensen theory. Our present benchmark data contribute to an improved understanding of the basic atomic collision processes and to numerous applications in nuclear physics. Extending the GANIL data [1] to higher accuracy and energies, we can now answer at which velocities the Bohr-Lindhard density effect in stopping will vanish.

Design and simulation of 4H-SiC low gain avalanche diode

In the applications of nuclear and high-energy physics, Silicon Low Gain Avalanche Diodes (Si LGAD) as particle detectors have been shown to perform well in time resolution. Compared with silicon, 4H Silicon Carbide (4H-SiC) has a wider band gap, better radiation resistance, higher saturated carrier velocity and lower temperature sensitivity. Therefore, 4H-SiC LGAD is suitable for the detection of Minimum Ionization Particles (MIPs) under extreme radiation and temperature. However, due to the complexity of SiC device design and production, high performance SiC LGAD devices have not yet been produced. In this work, we use TCAD tools to design and simulate two n-type 4H-SiC LGAD structures with different electric field profiles, I/V and C/V characteristics and gain efficiencies. Through the analysis of simulation results, the LGAD with a triangle electric field profile in the gain layer has a higher gain efficiency, while the design with a trapezoid electric field profile is less affected by the gain layer thickness and more stable at high temperature. Subsequently, we will develop a set of SiC production processes under the guidance of this work.