Machine learning in nuclear physics at low and intermediate energies
Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …
various research fields due to its exciting and powerful capability of modeling tools used for …
Prediction of nuclear charge density distribution with feedback neural network
TS Shang, J Li, ZM Niu - Nuclear Science and Techniques, 2022 - Springer
Nuclear charge density distribution plays an important role in both nuclear and atomic
physics, for which the two-parameter Fermi (2pF) model has been widely applied as one of …
physics, for which the two-parameter Fermi (2pF) model has been widely applied as one of …
Integrated photonuclear cross sections in the giant dipole resonance of odd-mass actinide nuclei
This study explores the integrated total photonuclear cross section (σ 0) within the context of
the giant dipole resonance (GDR) in odd-mass actinide nuclei. Using artificial neural …
the giant dipole resonance (GDR) in odd-mass actinide nuclei. Using artificial neural …
Magnetic Moments of Odd A Nuclei in q-Deformed Nuclear Shell Model
P Feng, D Lianrong - Chinese Physics C, 1996 - cpc.ihep.ac.cn
q-deformed nuclear magnetic moment operatoris defined in terms of rank 1tensor operator
of the quantum algebra SU q (2). The results show that the q-deformation parameter …
of the quantum algebra SU q (2). The results show that the q-deformation parameter …
Nuclear mass predictions based on a convolutional neural network
A convolutional neural network (CNN) is employed to investigate nuclear mass. By
introducing the masses of neighboring nuclei and the paring effects at the input layer of the …
introducing the masses of neighboring nuclei and the paring effects at the input layer of the …
Inference of parameters for the back-shifted Fermi gas model using a feedforward neural network
The back-shifted Fermi gas model is widely employed for calculating nuclear level density
(NLD) as it can effectively reproduce experimental data by adjusting parameters. However …
(NLD) as it can effectively reproduce experimental data by adjusting parameters. However …
Polarization effects on the rotational gyromagnetic ratio and magnetic dipole moments of 175,177,177mYb
The measured ground-and isomeric-state magnetic moments of 175,177 Yb have been
theoretically investigated for the first time using the method based on the Quasiparticle …
theoretically investigated for the first time using the method based on the Quasiparticle …
Understanding the relationship between electric dipole polarizability and photonuclear (-2) moment in odd-A actinide nuclei
The nuclear electric dipole (E1) polarizability (α E1) is mainly dominated by the dynamics of
the giant dipole resonance (GDR). α E1 is proportional to the (-2) moment of the total photo …
the giant dipole resonance (GDR). α E1 is proportional to the (-2) moment of the total photo …
Inference and visualization of nuclear magnetic moment studies with neuro-fuzzy systems
This study aims to predict the magnetic moments of nuclei with odd-A numbers in a certain
region of which the magnetic moment has not yet been calculated, using the Adaptive Neuro …
region of which the magnetic moment has not yet been calculated, using the Adaptive Neuro …
[HTML][HTML] Magnetization in iron based compounds: A machine learning model analysis
In material science domain, the data availability has made it possible to design and test
machine learning models not only to strengthen our understanding of various properties of …
machine learning models not only to strengthen our understanding of various properties of …