Investigating the M1 Radiative Decay Behaviors and the Magnetic Moments of the Predicted Triple-Charm Molecular-Type Pentaquarks
PHYSICAL REVIEW D(2024)
Abstract
In this work, we systematically study the electromagnetic properties including the M1 radiative decay widths and the magnetic moments of the isoscalar $\Xi_{c c} D^{(*)}$, $\Xi_{cc}D_{1}$, and $\Xi_{cc}D_{2}^{*}$ triple-charm molecular-type pentaquark candidates, where we adopt the constituent quark model and consider both the $S$-$D$ wave mixing effect and the coupled channel effect. Our numerical results suggest that the M1 radiative decay widths and the magnetic moments of the isoscalar $\Xi_{c c} D^{(*)}$, $\Xi_{cc}D_{1}$, and $\Xi_{cc}D_{2}^{*}$ triple-charm molecular-type pentaquark candidates can reflect their inner structures, and the study of the electromagnetic properties is the important step to construct the family of the triple-charm molecular-type pentaquarks. With the accumulation of the experimental data during the high-luminosity phase of LHC, we expect that the present work combined with the corresponding mass spectrum information can encourage the experimental colleagues at LHCb to focus on the isoscalar $\Xi_{c c} D^{(*)}$, $\Xi_{cc}D_{1}$, and $\Xi_{cc}D_{2}^{*}$ triple-charm molecular-type pentaquark candidates.
MoreTranslated text
Key words
Dipole Interactions
PDF
View via Publisher
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
Related Papers
2009
被引用63 | 浏览
2012
被引用278 | 浏览
2015
被引用40 | 浏览
2018
被引用36 | 浏览
2018
被引用8 | 浏览
2020
被引用15 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话