Single-Neutron Adding on ^34S

A. N. Kuchera,C. R. Hoffman，C. Wibisono,I. Wiedenhöver

EUROPEAN PHYSICAL JOURNAL A（2024）

Davidson College | Argonne National Laboratory | Florida State University | Ursinus College

Cited 0|Views10

Abstract

Single-neutron adding data was collected in order to determine the distribution of the single-neutron strength of the 0f_7/2 , 1p_3/2 , 1p_1/2 and 0f_5/2 orbitals outside of Z=16, N=18 , ^34 S. The ^34 S(d,p) ^35 S reaction has been measured at 8 MeV/u to investigate cross sections to excited states in ^35 S. Outgoing proton yields and momenta were analyzed by the Super-Enge Split-Pole Spectrograph in conjunction with the CeBrA demonstrator located at the John D. Fox Laboratory at Florida State University. Angular distributions were compared with Distorted Wave Born Approximation calculations in order to extract single-neutron spectroscopic overlaps. Spectroscopic overlaps and strengths were determined for states in ^35 S up through 6 MeV in excitation energy. Each orbital was observed to have fragmented strength where a single level carried the majority. The single-neutron centroids of the 0f_7/2 , 1p_3/2 , 1p_1/2 and 0f_5/2 orbitals were determined to be 2360^+90_-40 keV, 3280^+80_-50 keV, 4780^+60_-40 keV, and ≳ 7500 keV, respectively. A previous discrepancy in the literature with respect to the distribution of the neutron 1p_1/2 strength was resolved. The integration of the normalized spectroscopic strengths, up to 5.1 MeV in excitation energy, revealed fully-vacant occupancies for the 0f_7/2 , 1p_3/2 , and 1p_1/2 orbitals, as expected. The spacing in the single-neutron energies highlighted a reduction in the traditional N=28 shell-gap, relative to both the 1p spin-orbit energy difference ( N=32 ) and the lower limit on the N=34 shell spacing.

Translated text

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper