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Artificial-intelligence-supported shell-model calculations for light Sn isotopes
(American Physical Society, 11/04/2022)
The region around the doubly magic nuclide
100
Sn
is very interesting for nuclear physics studies in terms of structure, reaction, and nuclear astrophysics. The main ingredients in nuclear structure studies using the ...
Neural network estimations of annealed and non-annealed Schottky diode characteristics at wide temperatures range
(Elsevier, 15/06/2022)
In this study, Artificial Neural Network (ANN) model has been proposed to characterize the annealed and the non-annealed Schottky diode from experimental data. The experimental current values of Ni/n-type 6H–SiC Schottky ...
Energy transitions and half-life determinations of Sc isotopes from photonuclear reaction on Ti target
(World Scientific Publishing Company, 07/03/2022)
For the activation of the target nucleus, bremsstrahlung photons generated in a medical linear accelerator can be used. Due to particle separation energy limitations for low photon energies in the giant resonance region ...
MODELING OF PATELLA HEIGHT WITH DISTAL FEMUR AND PROXIMAL TIBIA REFERENCE POINTS WITH ARTIFICIAL NEURAL NETWORK
(25/03/2022)
The patellofemoral joint is one of the parts of the knee extension mechanism that plays a role in
the stability of the knee by enlarging the force arm of the quadriceps muscle and changing the
direction of the muscle ...
Performance of machine learning algorithms on neutron activations for Germanium isotopes
(2023/7/1)
In the studies of nuclear physics, one of the important parameters for nuclear reactions is the reaction cross-section. It can be obtained from experimental data or by different theoretical models. In this study, we implement ...
Neutron Single-Particle States in 101Sn by Polynomial Fits and Shell Model Calculations for Light Sn Isotopes
(2024/2)
The neutron single-particle energies (SPEs) in 101Sn are one of the main ingredients needed in nuclear studies in the region around the doubly magic 100Sn nucleus. Due to the lack of experimental data on 101Sn spectrum, ...
Estimation of fission barrier heights for even–even superheavy nuclei using machine learning approaches
(2023/3/21)
With the fission barrier height information, the survival probabilities of super-
heavy nuclei can also be reached. Therefore, it is important to have accurate
knowledge of fission barriers, for example, the discovery ...
Predicting -decay energy with machine learning
(2023/3/15)
Q β represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown ...
Estimation of the S34 (0) S-factor for 3He (α, γ) 7Be reaction by using distorted wave born approximation and artificial neural network
(2023/9/6)
The astrophysical S-factor and total cross-section of radiative capture reaction are analyzed using the first-order distorted wave born approximation and artificial neural network. To make estimations of S 34 (0) at ...
Applications of different machine learning methods on nuclear charge radius estimations
(2023/11/21)
Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, ...