4.6 Article

Studying differential cross section for elastic proton scattering by a tensor model

Related references

Note: Only part of the references are listed.
Article Physics, Multidisciplinary

Determination of Photonuclear Reaction Cross-Sections on Stable P-shell Nuclei by Using Deep Neural Networks

Serkan Akkoyun et al.

Summary: Photonuclear reactions play a crucial role in the study of nuclear structure, and accurate determination of cross-sections is essential for experimental investigations. In this study, neural network methods were used to estimate (gamma, n) photonuclear reaction cross-sections for stable p-shell nuclei. The goal was to find the optimal neural network structures for accurate estimations and compare them with existing data. The results showed that deep neural network structures were more convenient for this task. Notably, tanh activation function outperformed ReLU for shallow NN models, but as the models became deeper, the difference between the two decreased considerably. It was concluded that the size of the hidden layer and the number of neurons in each layer were crucial hyperparameters in this context.

BRAZILIAN JOURNAL OF PHYSICS (2023)

Article Physics, Nuclear

Estimation of fission barrier heights for even-even superheavy nuclei using machine learning approaches

Cafer Mert Yesilkanat et al.

Summary: In this study, five machine learning techniques were used to accurately predict the fission barriers of 330 even-even super-heavy nuclei. The results showed that the Cubist model, support vector regression, and extreme gradient boosting methods generally provided better results and could be a better tool for estimating fission barrier heights.

JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS (2023)

Article Chemistry, Inorganic & Nuclear

Estimations for (n, α) reaction cross sections at around 14.5MeV using Levenberg-Marquardt algorithm-based artificial neural network

Hasan Ozdogan et al.

Summary: This research presents a new approach using artificial neural network (ANN) to predict neutron-induced reaction cross-sections. The Levenberg-Marquardt algorithm-based ANN is shown to be well suited for this purpose, with high correlation coefficients. The results are compared to theoretical calculations of TALYS 1.95 nuclear code, demonstrating the usability of the ANN model.

APPLIED RADIATION AND ISOTOPES (2023)

Article Chemistry, Physical

Performance of machine learning algorithms on neutron activations for Germanium isotopes

Rihab Gargouri et al.

Summary: In this study, machine learning algorithms were used to perform regression analysis on the nuclear cross-section of neutron-induced nuclear reactions of Germanium isotopes. Three machine learning models (ANN, KNN, and SVM) were developed and trained using data from the TENDL-2019 library. The performance of each algorithm was evaluated and compared using mean square error (MSE) and correlation coefficient (R2). The results showed that machine learning techniques can safely obtain cross-section information and the regression curves generated by our models are in good agreement with the evaluated nuclear data library. From the study, it was found that ANN and KNN models perform better compared to the SVM algorithm. Machine learning models can complement classical physics-guided models and have potential applications in nuclear data analyses, serving as an alternative method for estimating cross-sections for neutron energies of unknown values.

RADIATION PHYSICS AND CHEMISTRY (2023)

Article Chemistry, Inorganic & Nuclear

Mass excess estimations using artificial neural networks

H. Ozdogan et al.

Summary: This study proposes a new algorithm based on artificial neural networks (ANN) to determine the mass excess of atomic nuclei. Experimental data and the Levenberg-Marquardt algorithm were used to train and validate the model. The results of the study suggest that ANN can be used to calculate estimates of mass information.

APPLIED RADIATION AND ISOTOPES (2022)

Article Nuclear Science & Technology

Adjustment of JEFF-3.3 data for U-235 and Pu-239 in the fast, non-resonant energy range

Sandro Pelloni et al.

Summary: In this study, a stochastic technique based on the Serpent code is used to adjust the nuclear data for U-235 and Pu-239. The adjustment is done by using the JEFF-3.3 nuclear data library, which includes covariance data, and the Asymptotic Generalized Linear Least-Squares (AGLLS) assimilation methodology. The results show that the adjusted parameters include a decrease in the fission and elastic scattering cross-sections for U-235, an increase in (nu) over bar, and a decrease in the elastic scattering cross-section for Pu-239.

ANNALS OF NUCLEAR ENERGY (2022)

Article Physics, Multidisciplinary

Current nuclear data needs for applications

Karolina Kolos et al.

Summary: Accurate nuclear data is crucial for advances in various fields, and outdated or incomplete data can hinder progress and compromise safety. Collaboration across organizations and international borders is essential in addressing the shared needs for nuclear data.

PHYSICAL REVIEW RESEARCH (2022)

Article Physics, Nuclear

Elastic proton scattering off nonzero spin nuclei

Matteo Vorabbi et al.

Summary: This study constructs a microscopic optical potential using modern approaches based on chiral theories and applies it to nuclei with ground states characterized by non-zero spin-parity quantum numbers. The results show remarkable agreement with experimental data and provide reliable estimates for theoretical uncertainties.

PHYSICAL REVIEW C (2022)

Article Nuclear Science & Technology

Nuclear data evaluation augmented by machine learning

Pedro Vicente-Valdez et al.

Summary: This study proposes leveraging Machine Learning and Artificial Intelligence to support nuclear data evaluation, developing two ML models to infer nuclear data, and demonstrates that ML models can more accurately match new experimental measurements, aiding traditional physics-guided models.

ANNALS OF NUCLEAR ENERGY (2021)

Article Chemistry, Multidisciplinary

Generation of Proton- and Alpha-Induced Nuclear Cross-Section Data via Random Forest Algorithm: Production of Radionuclide 111In

Mohamad Amin Bin Hamid et al.

Summary: This study investigated the generation of proton and alpha-induced nuclear cross-section data for the production of Indium-111 (In-111) in nuclear medicine applications. Nuclear cross-section data were generated using a random forest algorithm and experimental nuclear cross-section data from the EXFOR database, with the ENDF/B-VII.0 library as the benchmark. The study successfully produced reasonably accurate regression curves for nuclear cross-section data.

APPLIED SCIENCES-BASEL (2021)

Article Physics, Nuclear

Impact of three-body forces on elastic nucleon-nucleus scattering observables

Matteo Vorabbi et al.

Summary: The purpose of this study is to introduce the 3N force into the dynamic part of the OP for a more consistent calculation. Through an approximate treatment of the 3N force in the model, the major contribution of the two-pion exchange term to the 3N force is identified, and the convergence of results at the next-to-next-to-next-to-leading-order is confirmed.

PHYSICAL REVIEW C (2021)

Review Physics, Nuclear

The joint evaluated fission and fusion nuclear data library, JEFF-3.3

A. J. M. Plompen et al.

EUROPEAN PHYSICAL JOURNAL A (2020)

Review Physics, Nuclear

Our Future Nuclear Data Needs

Lee A. Bernstein et al.

ANNUAL REVIEW OF NUCLEAR AND PARTICLE SCIENCE, VOL 69 (2019)

Article Transportation Science & Technology

A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation

Xinyu Chen et al.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)

Article Physics, Nuclear

IAEA CIELO Evaluation of Neutron-induced Reactions on 235U and 238U Targets

R. Capote et al.

NUCLEAR DATA SHEETS (2018)

Article Instruments & Instrumentation

The experimental nuclear reaction data (EXFOR): Extended computer database and Web retrieval system

V. V. Zerkin et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2018)

Article Mathematics, Applied

Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety

Pavel Grechanuk et al.

JOURNAL OF COMPUTATIONAL AND THEORETICAL TRANSPORT (2018)

Article Instruments & Instrumentation

A modified Generalized Least Squares method for large scale nuclear data evaluation

Georg Schnabel et al.

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT (2017)

Article Radiology, Nuclear Medicine & Medical Imaging

Nuclear data for production and medical application of radionuclides: Present status and future needs

Syed M. Qaim

NUCLEAR MEDICINE AND BIOLOGY (2017)

Article Physics, Nuclear

Theoretical optical potential derived from nucleon-nucleon chiral potentials

Matteo Vorabbi et al.

PHYSICAL REVIEW C (2016)

Article Physics, Nuclear

Bayesian Monte Carlo method for nuclear data evaluation

A. J. Koning

EUROPEAN PHYSICAL JOURNAL A (2015)

Article Physics, Nuclear

Elastic scattering of protons from 9C with a 290 MeV/nucleon 9C beam

Y. Matsuda et al.

PHYSICAL REVIEW C (2013)

Article Physics, Nuclear

Modern Nuclear Data Evaluation with the TALYS Code System

A. J. Koning et al.

NUCLEAR DATA SHEETS (2012)