Evolution of high-spin states in neutron-rich ${}^{195-202}$Au isotopes approaching the $N=126$ shell closure

Abstract

\textbf{Background: } The nuclear structure in the region southwest of the doubly magic ${}^{208}$Pb is important to benchmark theoretical models relevant for neutron-rich heavy element formation and for the origin of the $A\approx 195$ peak in the mass abundance distribution. Although neutron-rich Pb, Tl, and Hg ($Z=80-82$) isotopes are relatively well studied, experimental data for neutron-rich Au isotopes ($Z=79$) remain largely limited due to experimental challenges. \textbf{Purpose: } The purpose of this work is to investigate the evolution of the high-spin structure in neutron-rich Au isotopes near the $N=126$ shell closure and to characterize the underlying shell-model configurations, with a particular focus on the roles of the high-$j$ unique-parity $\pi h_{11/2}$ and $\nu i_{13/2}$ orbitals. \textbf{Methods: } Neutron-rich ${}^{195-202}$Au isotopes were produced using multi-nucleon transfer reactions of ${}^{198}$Pt(${}^{136}$Xe, ${}^x$I)${}^y$Au at a beam energy of 7 MeV/u. %between a 7 MeV/u ${}^{136}$Xe beam and a ${}^{198}$Pt target at GANIL. Prompt and delayed $\gamma$-ray spectroscopy of isotopically identified reaction products was performed with a unique experimental setup combining the VAMOS++ large acceptance magnetic spectrometer, the AGATA HPGe $\gamma$-ray tracking array, and the CATLIFE detection system.

due to limit in abstract length, result and conclusion part in abstract is removed

References in corpus (8)

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• AGATA - Advanced Gamma Tracking Array
• Multinucleon transfer reactions in closed-shell nuclei
• Beta Decays of Isotones with Neutron Magic Number of N=126 and R-process Nucleosynthesis
• Dual Position Sensitive MWPC for tracking reaction products at VAMOS++
• Fast trajectory reconstruction techniques for the large acceptance magnetic spectrometer VAMOS++
• Nuclear structure properties of ${}^{193-200}$Hg isotopes within large-scale shell model calculations
• Particle Identification at VAMOS++ with Machine Learning Techniques