Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1409.6426

Pairing effect in thermal shape fluctuation model on the width of giant dipole resonance

A. K. Rhine Kumar·P. Arumugam·N. Dinh Dang
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present an approach that includes temperature-dependent shell effects and fluctuations of the pairing field in the thermal shape fluctuation model (TSFM). We apply this approach to study the width of giant dipole resonance (GDR) in $^{97}$Tc, $^{120}$Sn and $^{208}$Pb. Our results demonstrate that the TSFM that includes pairing fluctuations can explain the recently observed quenching in the GDR width. We also show that to validate pairing prescriptions and the parameters involved, we require more and precise data.

Explore related subjects

nucl-th

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. K. Rhine Kumar, P. Arumugam, N. Dinh Dang. 2014-09-23. Pairing effect in thermal shape fluctuation model on the width of giant dipole resonance. https://doi.org/10.1103/physrevc.90.044308

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Qcombo: A Python Package for Automated Commutator Calculations of Quantum Many-Body Operators

qcombo is a Python package for the symbolic evaluation of commutators between general quantum many-body operators expressed in normal-ordered form using the generalized Wick theorem. The package provides an automated and systematic framework for generating the corresponding algebraic expressions, significantly reducing the risk of human error in lengthy and complex analytical derivations. It is designed to assist the development and implementation of modern many-body methods in nuclear physics, quantum chemistry, and related fields. The functionality and workflow of the package are demonstrated through an application to the in-medium similarity renormalization group (IMSRG) method, which has been widely used for nuclear ab initio calculations. As a representative example, qcombo is employed to automatically generate the complete set of multi-reference IMSRG flow equations with operators truncated at the normal-ordered three-body level.

nucl-th↗
arXiv2026

Intruder-driven mirror energy differences between $^{29}$Cl and $^{29}$Mg studied with antisymmetrized molecular dynamics

To clarify the mirror energy differences (MEDs) of the proton-unbound nucleus $^{29}$Cl and their microscopic origins, we investigate the low-lying states of the $^{29}$Cl-$^{29}$Mg mirror pair using antisymmetrized molecular dynamics. The calculation reasonably reproduces the normal and intruder states of $^{29}$Mg, while suggesting alternative spin-parity assignments for $^{29}$Cl. The $1/2^+$ and $3/2^+$ states are predicted to form a nearly degenerate ground-state doublet with a small MED because of their similar intrinsic structures. In contrast, the $3/2^-$ and $7/2^-$ intruder states exhibit large negative MEDs and are assigned to the observed resonances at approximately 500~keV and 1.1~MeV, respectively. Their large MEDs originate from the reduced Coulomb energies associated with the stronger deformation and spatially extended proton distributions in the intruder configurations.

nucl-th↗
arXiv2026

Advancing Nuclear Physics with Machine Learning and Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress in AI-assisted studies of nuclear structure and reaction observables, heavy-ion collisions and dense nuclear matter properties, many-body wave functions, experimental facilities and data analysis. This review focus on new progress since the last review in 2023, and machine learning in nuclear physics is evolving from purely data inferences to physics informed learning. Future directions on the integration of physical knowledge with modern learning architectures, large foundation models and other emerging methods are also reviewed. These developments suggest that AI is enabling and advancing new approaches towards most challenging nuclear physics problems.

nucl-th↗