Nuclear Database Research Articles

The high-precision detector of the JUNO-TAO experiment

The Taishan Antineutrino Observatory (TAO) is a proposed ton scale liquid scintillator (LS) detector designed to precisely measure the reactor neutrino energy spectrum with the highest possible energy resolution. This will provide a reference spectrum for Jiangmen Underground Neutrino Observatory (JUNO) and a benchmark to verify the nuclear database. As a satellite experiment of JUNO, TAO will be installed near the reactor core at a distance of ∼30m. The detector uses 2.8m3 of Gd-doped liquid scintillator (∼1 ton fiducial volume, Gd-LS) contained in a spherical acrylic vessel. To maximize the photon collection efficiency in the detector, a 10m2 SiPM array is proposed to fully cover the acrylic vessel and collect as many scintillation photon as possible. The photon detection efficiency of SiPMs should be larger than 50%, in order to achieve the desired energy resolution (1.5%/E, photon statistical resolution). Additionally, the SiPMs will be operated at low temperature (−50 °C or lower) to reduce dark noise. Meanwhile, a shield and muon veto system will be located outside of the neutrino detector to control the environmental background. An overview of the JUNO-TAO experiment and its current progress are discussed.

Local charge radius relations based on neutron shell effects

In this paper, we obtained the mean mass density parameter (MMDP) [Formula: see text] based on the nuclear mass AME2020 and nuclear charge radius CR2013 databases. On this basis, we study the dependence relationship between neutron number N and the two neighboring isotopes MMDP difference [Formula: see text], and then obtained the empirical formula for [Formula: see text]. By combining this empirical formula with the AME2020 and CR2013 databases, the root-mean-square deviation (RMSD) for 606 nuclear charge radii with [Formula: see text] 21 between the calculated and experimental values is 0.0046[Formula: see text]fm. Considering the odd–even staggering and isospin term correction, the study found that the RMSD is reduced to 0.0040[Formula: see text]fm. This work also uses Levenberg–Marquart (L-M) neural network approach to study the nuclear charge radius. The RMSD of nuclear charge radius is only 0.0030 fm, where the accuracy is increased by about 25%. Furthermore, the predicted values of nuclear charge radius obtained using this modified empirical formula and Neural Network model are competitive with the results of other models. These results indicate that nuclear charge radius relation and Neural Network model based on [Formula: see text] proposed in this work have a certain accuracy, simplicity and reliability.

Impact of reactor neutrino uncertainties on coherent scattering's discovery potential

Abstract Nuclear power reactors are the most intense man-made source of antineutrino's and have long been recognized as promising sources for coherent elastic neutrino-nucleus scattering (CE$\nu$NS) studies. Its observation and the spectral shape of the associated recoil spectrum is sensitive to a variety of exotic new physics scenarios and many experimental efforts are underway. Within the context of the reactor antineutrino anomaly, which initially indicated eV-scale sterile neutrino's, the modeling of the reactor antineutrino spectrum has seen a significant evolution in the last decade. Even so, uncertainties remain due to a variety of nuclear structure effects, incomplete information in nuclear databases and fission dynamics complexities. Here, we investigate the effects of these uncertainties on one's ability to accurately distinguish new physics signals. For the scenarios discussed here, we find that reactor spectral uncertainties are similar in magnitude to the projected sensitivities pointing towards a need for $\beta$ spectroscopy measurements below the inverse $\beta$ decay threshold.

Design and implementation of HDF5-format neutron nuclear database in RMC code

This study developed a new structured nuclear database to improve the readability and extensibility of the ACE (A Compact ENDF) nuclear database, save disk space, reduce memory usage, and enhance the computational efficiency of the Reactor Monte Carlo (RMC) code. A Python package was developed to store nuclear data in HDF5 format. Compared to the ACE database, the HDF5-format database shows significant improvements: an 80% reduction in disk space for continuous energy neutron data and a 60% reduction for neutron thermal scattering data. The HDF5-format database was implemented in RMC’s criticality calculation mode and validated through VERA benchmark problem 2B, demonstrating perfect agreement with the ACE results in keff and neutron flux counts. The Computational results indicate a 7.7% reduction in memory usage and a 20.1% improvement in computational efficiency with the HDF5-format database. Additional tests show that using the database at a single temperature point reduces memory usage by 5.4% and running time by 13.2%. At two temperature points, memory usage decreases by 35.2% and running time by 18.0%. The new data structure reduces temperature-independent redundant data and improves indexing efficiency, leading to greater savings with more temperature points. This development enhances performance of criticality calculation of RMC and addresses ACE database limitations.

Lead Slowing-Down Neutron Spectrometry II: Cross-Section Data for <sup>243</sup>Cm(<i>n</i>, <i>f</i>), <sup>244</sup>Cm(<i>n</i>, <i>f</i>), <sup>245</sup>Cm(<i>n</i>, <i>f</i>), <sup>246</sup>Cm(<i>n</i>, <i>f</i>), <sup>247</sup>Cm(<i>n</i>, <i>f</i>), <sup>248</sup>Cm(<i>n</i>, <i>f</i>) at Energies up to 100 keV

The possibilities of lead slowing-down neutron spectrometry have been demonstrated. We present a review of the results of a series of works on measuring the fission cross-sections of curium isotopes 243Cm, 244Cm, 245Cm, 246Cm, 247Cm, 248Cm by neutrons with energies below 100 keV, performed by a joint group of researchers of INR RAS and SSC RF IPPE on the SVZ-100 neutron spectrometer. This third-generation spectrometer has a high luminosity, which made it possible to study neutron-nuclear processes in microgram samples of radioactive nuclides, which is not available in experiments using time-of-flight spectrometry. The results of work at the INR RAS–SSC RF IPPE are reflected in international nuclear databases, complement the known experimental data and in some cases indicate the need to adjust the recommended values.

FECSG-ML: Feature Engineering for Nuclear Reaction Cross Sections Generation Using Machine Learning

In the field of nuclear science, obtaining and utilizing nuclear data, including nuclear reaction data, nuclear structure information, and radioactive decay data, is crucial. Neutron-induced nuclear reactions, particularly nuclear cross sections data, are essential for various applications, including reactor design. The EXFOR database is the only international repository for storing nuclear reaction experimental measurement information and data. However, experimental measurement data are often scarce, subject to discrepancies, or even errors, requiring human evaluation. This process can be prone to biases and significant uncertainties. To address these challenges, this study proposes a novel framework, Feature Engineering for Nuclear Reaction Cross Section Generation using Machine Learning (FECSG-ML), which employs machine learning methods to generate nuclear cross sections data, serving as a substitute for evaluating nuclear databases. Given the limited size of the EXFOR database, training a model solely on EXFOR data could lead to underfitting. Therefore, the proposed approach utilizes transfer learning, initially pre-training the model using the ENDF/B-VIII.0 dataset and subsequently fine-tuning it with the EXFOR database. This approach ensures high accuracy where real data are available and enables the learning of characteristics of the evaluation dataset where real data are lacking. Moreover, machine learning techniques are employed to transform discrete nuclear cross sections data into a continuous format, accommodating various isotopes and predicting multiple sets of cross sections data. The framework integrates various machine learning methods and utilizes ensemble learning for result optimization. Experimental results demonstrate that the regression curves generated by the FECSG-ML model align well with EXFOR data points, outperforming the ENDF/B-VIII.0 evaluation database. Furthermore, the nuclear cross sections data generated by the FECSG-ML model are applied in the OpenMC Monte Carlo simulation program to simulate pin fuel assemblies and CANDU reactors, confirming the effectiveness of the model. This study underscores the importance of accurate and reliable nuclear cross sections data and provides a method for substituting the evaluation of nuclear databases.

Activation cross-sections of proton-induced nuclear reactions on natural zinc in the energy range of 4–30 MeV

In the present work, the excitation functions for the proton-induced nuclear reactions on natural zinc were measured in the energy range of 4–30 MeV using the well-established stacked-foil activation procedure. The activation products were measured based on their characteristic gamma lines using HPGe γ-ray spectrometry. The radioactivities determined were used for the calculation of cross-sections of the radionuclides of interest, i.e., 61Cu, 62,65,69mZn, 57Co and 66,67,68Ga. The cross-sections have also been compared with the available literature data and the theoretical prediction of the TALYS code via its latest TENDL-2023 library as well as the prediction of EMPIRE-3.2.2 model code. The results show a reasonable agreement when compared with the available literature data. This work, however, shows that the theoretical data extracted from the TENDL-2023 library and EMPIRE-3.2.2 default calculation significantly underestimate the experimental cross-sections of 57Co nuclide while they overestimate those of 69mZn. The present result has potential applications to improve the predicting capability of the model codes as well as to serve as additional data for the nuclear reaction cross-section database.

Accurate pulse time distribution determination using MLEM algorithm in integral experiments

Integral experiments play a crucial role in advancing nuclear science and technology by providing critical data that validate theoretical models and enhance reactor designs. This study presents a novel approach to accurately determine pulse time distribution in integral experiments conducted with pulsed accelerators. By strategically placed monitors and shields at angles of 0° and 90° relative to the beam direction, neutron flight times from the target are measured, and a response matrix for neutron emission at different times is constructed through simulation. The Maximum Likelihood Expectation Maximization (MLEM) algorithm is employed for pulse time reconstruction, with the gamma ray flight time spectrum from monitors used as the initial spectrum to streamline the computational process. Experimental validation using a standard polyethylene sample and n-p scattering cross-sections confirms the accuracy of the method. Results are compared across multiple nuclear databases such as CENDL-3.2, ENDF/B-VIII.0, JENDL-5.0, and JEFF-3.3 libraries. The developed method significantly enhances the precision of pulse time distribution determination, thereby improving the quality and reliability of experimental data obtained from integral experiments conducted with pulsed accelerators.

The toolkit for nuclei library (TkN): a C++ interface to nuclear databases

The toolkit for nuclei library (TkN): a C++ interface to nuclear databases

A Generic Implementation of the D1S Methodology for Dose Rate Assessment by Monte Carlo Codes in Fusion Applications

Dose rate assessment is of utmost importance in fusion reactors because of the maintenance operations that these facilities will require. The standard way to perform this assessment in ITER is use of the Direct 1-Step (D1S) methodology, which consists of performing neutron transport simulation, material activation, and decay photon transport simulation in one step and thus in one simulation only. Usually, implementation of the D1S methodology requires changing the source files in a reference Monte Carlo code. The purpose of the present work is to develop an easy-to-implement method for Monte Carlo codes to help calculate the dose rate in fusion reactors. To do so, the proposal is to act on nuclear data only and not on source files of the simulation codes. This is done by replacing prompt photon production in evaluation files by suitable decay photon production, taking into account radioactive decays of radionuclides and irradiation history. This study was to be applied first on the TRIPOLI-4® Monte Carlo code for the sake of simplicity. TRIPOLI-4 is the reference code for particle transport at CEA. The verification and validation process relies first on a comparison to the reference Rigorous 2-Step (R2S) methodology and then on an experiment, the so-called Frascati Neutron Generator (FNG) dose experiment. The nuclear database to be changed is of the ENDF-6 format, a recurrent format in neutronics studies. The analysis studied both neutron and photon responses to check if the simulation was performing normally in a physical way and to compare the results with references provided by simulations based on the R2S methodology or by the FNG dose experiment. The simulations have proven to be in good agreement with the experimental results.

Thermodynamic description of U(IV) solubility and hydrolysis in chloride systems: Pitzer activity model for the system U4+–Na+–Mg2+–Ca2+–H+–Cl––OH––H2O(l)

This study presents updated chemical, thermodynamic, and activity models for the system U4+–Na+–Mg2+–Ca2+–H+–Cl––OH––H2O(l) derived using the Pitzer formalism and a strict ion interaction approach. The models build on comprehensive solubility datasets in dilute to concentrated NaCl, MgCl2, and CaCl2 solutions. The Nuclear Energy Agency-Thermochemical Database (NEA-TDB) selection of solubility and hydrolysis constants in the reference state were taken as anchoring point, and were extended further with the solid nanocrystalline phase UO2∙H2O(ncr) and the ternary complex Ca4[U(OH)8]4+. The former was identified in long-term solubility experiments at ambient conditions, whereas the latter has been selected in analogy to Th(IV), Np(IV), and Pu(IV) considering experimental evidences available for these An(IV) in alkaline, concentrated CaCl2 solutions. These models represent an improved tool for the calculation of U(IV) solubility and aqueous speciation in a variety of geochemical conditions including concentrated brine systems relevant in salt-based repositories for nuclear waste disposal.

The combination effect of optical potential and nuclear level density for alpha induced reaction cross sections on 92,94,95Mo isotopes

The theoretical studies of charged particle induced reactions are important to improve and study the radioisotope production, especially in reactions where the experimental data are incomplete or insufficient. In this study, cross section of alpha induced of 92,94,95Mo isotopes are calculated and analyzed by using different alpha optical model potentials and nuclear level density models with TALYS code. The effects of alpha optical model potentials and nuclear level density are analyzed separately and together for each reaction. To determine the best combination of models, chi-squared values are calculated for all combination cases. The commonly used program is TALYS to calculate the reaction cross section. For the calculations, the latest version of TALYS nuclear code version 1.96 is used. The experimental data are taken carefully for all reactions from experimental nuclear reaction data base (EXFOR). The obtained results are compared with these data and discussed. A good agreement between the calculated and experimental data is clearly presented for all analyzed reactions. It is seen from the results that alpha optical model potentials and nuclear level density models have an effective role on cross section calculations of alpha induced reactions.

Transport parameters from AMS-02 F/Si data and fluorine source abundance

The AMS-02 collaboration recently released cosmic ray F/Si data with an unprecedented accuracy. Cosmic ray fluorine is predominantly produced by fragmentation of heavier progenitors, while silicon is mostly accelerated at source. This ratio is thus maximally sensitive to cosmic ray propagation. We study the compatibility of the transport parameters derived from the F/Si ratio with those obtained from the lighter Li/C, Be/C, and B/C ratios. We also inspect the cosmic ray source abundance of F, which is one of the few elements that has a high first ionisation potential but is only moderately volatile and is a potentially key element to study the acceleration mechanism of cosmic rays. We used the 1D diffusion model implemented in the code and performed $ analyses accounting for several systematic effects (energy correlations in data, nuclear cross sections, and solar modulation uncertainties). We also took advantage of the nuclear database to update the F production cross sections for its most important progenitors (identified to be 56Fe, 32S, 28Si, 27Al, 24Mg, 22Ne, and 20Ne). The transport parameters obtained from AMS-02 F/Si data are compatible with those obtained from AMS-02 (Li,Be,B)/C data. The combined fit of all of these ratios leads to a $ 1.1$, with $ 10<!PCT!>$ adjustments of the B and F production cross sections (which are based on very few nuclear data points and would strongly benefit from new measurements). The F/Si ratio is compatible with a pure secondary origin of F, with a best-fit relative source abundance F/^ Si) CRS $ and an upper limit of $ $. Unfortunately, this limit is not sufficient to test global acceleration models of cosmic ray nuclei, for which values at the level of $ $ are required. Such levels could be attained with F/Si data with a few percent of accuracy at a few tens of TV, which is possibly within reach for the next generation of cosmic ray experiments.

Evaluating nuclear charge radii based on the mean mass-density parameter using BP neural networks

In this paper, we have successfully obtained the mean mass-density parameter ([Formula: see text]) using the nuclear masses AME2020 database and nuclear charge radius (CR) CR2013 database. The empirical formula is derived based on the relationship between [Formula: see text] and [Formula: see text] (the ratio of the number of neutrons to protons). Subsequently, we obtained an empirical formula for the difference in [Formula: see text] between two neighboring isotopes. By utilizing this empirical formula along with the AME2020 and CR2013 databases, we then calculated 625 charge radii for nuclei with [Formula: see text]. The root-mean-square deviation (RMSD) between the calculated values and the experimental values in the CR2013 database is 0.0075[Formula: see text]fm. The predicted nuclear CR values are comparable to those of other researches, which some predictions closely matching the experimental values measured in recent years. Additionally, this work used the Back Propagation (BP) neural network to establish a model for describing and predicting the difference in the mean mass-density parameter between two neighboring isotopes. The RMSD between the calculated and experimental values obtained using this model is 0.0039[Formula: see text]fm. Some of our predicted values have good accuracy and compared well with experimental values. Both of the above methods indicate that the nuclear CR relationship proposed in this paper based on the difference in mean mass-density parameter has simplicity and reliability.

Nuclear decay database in fission product mass region* *Supported by the National Key R&D Program of China (2022YFA1602000)

Accurate and reliable nuclear decay databases are essential for fundamental and applied nuclear research studies. However, decay data are not usually as accurate as expected and need improvement. Hence, a new Chinese nuclear decay database in the fission product mass region (A = 66−172) based on several major national evaluated data libraries has been developed under joint efforts in the CNDC working group. A total of 2358 nuclides have been included in this decay database. Two main data formats, namely ENSDF and ENDF, have been adopted. For the total mean β and γ energies, available data from total absorption gamma ray spectroscopy measurements have been adopted. For some nuclides without experimental measurements, theoretically calculated values have been added.

Design of the ASIC readout scheme for the JUNO-TAO experiment

The Taishan Antineutrino Observatory (TAO) has been proposed to precisely measure the reactor antineutrino spectrum with an energy resolution better than 2% at 1 MeV. It can provide a reference spectrum to the Jiangmen Underground Neutrino Observatory (JUNO) to enhance its sensitivity to the neutrino mass ordering measurement. In addition, TAO can also provide a benchmark to verify the nuclear database and conduct new physics searches. The TAO detector is a 2.8-ton liquid scintillator detector equipped with a 10 m2 silicon photomultiplier (SiPM) array to collect the light with a coverage of 94%. The TAO detector will be operated at -50 °C to suppress the SiPM dark count rate. In this work, we report on the design of a readout system based on the KLauS6 chip to handle the outputs from the SiPMs, which exhibits advantages of high granularity (potentially good particle identification) and large dynamic range. A dedicated mockup system has been built and its performance carefully evaluated both at room and low temperatures, measuring gain, charge linearity, signal-to-noise ratio, and dead time. The testing results show that the performance of this system can meet the requirements of TAO. However, an abnormal increase in current at 1.8 V analog power of the KLauS6 chip has been observed when operating it at temperatures below -20 °C, resulting in a potential risk on the long-term reliability. Therefore, the KLauS-based readout design is considered as an alternative option for the future upgrading of the TAO's readout system. This study also provides a reference and guidance for other relevant applications.

Fully discrete model of kinetic ion-induced electron emission from metal surfaces

Ion-induced electron emission (IIEE) is an important process whereby ions impinging on a material surface lead to net emission of electrons into the vacuum. While relevant for multiple applications, IIEE is a critical process of electric thruster (ET) operation and testing for space propulsion, and, as such, it must be carefully quantified for safe and reliable ET performance. IIEE is a complex physical phenomenon, which involves a number of ion-material and ion-electron processes, and is a complex function of ion mass, energy, and angle, as well as host material properties, such as mass and electronic structure. In this paper, we develop a discrete model of kinetic IIEE to gain a more accurate picture of the electric thruster chamber and facility material degradation processes. The model is based on three main developments: (i) the use of modern electronic and nuclear stopping databases, (ii) the use of the stopping and range of ions in matter to track all ion and recoil trajectories inside the target material, and (iii) the use of a scattering Monte Carlo approach to track the trajectories of all mobilized electrons from the point of first energy transfer until full thermalization or escape. This represents a substantial advantage in terms of physical accuracy over existing semi-analytical models commonly used to calculate kinetic IIEE. We apply the model to Ar, Kr, and Xe irradiation of W and Fe surfaces and calculate excitation spectra as a function of ion depth, energy, and angle of incidence. We also obtain minimum threshold ion energies for net nonzero yield for each ion species in both Fe and W and calculate full IIEE yields as a function of ion energy and incidence angle. Our results can be used to assess the effect of kinetic electron emission in models of full ET facility testing and operation.

Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images.

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

Production of 203Pb from enriched 205Tl using deuteron beams

Lead-203 is a SPECT emitter that can be used in theranostic applications as an imaging counterpart of lead-212 which is intended to be used for alpha therapy as lead-212/bismuth-212 in-vivo generator. In our study, we explore the production of lead-203 using enriched thallium-205 target irradiated by a deuteron beam. Excitation functions of deuteron induced reactions leading to the formation of 204m,203m2+m1+g,202m,201m+gPb, 202Tl and 203m+gHg isotopes were determined experimentally in the energy range from 21 MeV to 34 MeV. Cross sections were measured using the stacked foils technique and a set of two monitor foils, natNi and natTi for beam intensity evaluation. The experimental excitation functions of the investigated reactions were compared with the published data and also with the TENDL-2021 nuclear database. From our experimental data, we calculated lead-203 thick target yield in the energy range between 30 MeV and 32.5 MeV to be 56.7 MBq/μAh ±6.1 MBq/μAh. This value is compatible with large batch production showing that deuteron beams can be used for a routine production process. However, special attention must be paid to 203Hg and other lead contaminants.

Developing a New Web Service for Experimental Nuclear Reaction Database (EXFOR) Using RESTful API and JSON

Efficient data mining from the Experimental Nuclear Reaction Database (EXFOR) has a potential for utilization of modern computational analysis techniques to find trends, shortcomings and hidden patterns in the database, which in turn helps improve our knowledge of nuclear physics. To facilitate the data mining, we have developed two EXFOR parsing computer programs (EXFOR Parser) to convert the data in the database stored in the EXFOR format into the widely adopted JSON format. The converted JSON data are used for further processing to extract individual physical observables and generate tabulated data (x, y, dx, dy) where all units of measurement are standardized. Furthermore, we have developed REST APIs and an open web system for easy access and quick visualizations of these converted datasets.