Chrome Extension
WeChat Mini Program
Use on ChatGLM

Comparison of recoil polarization in the 12C(e,ep) process for protons extracted from s and p shell

T. Kolar,S. PaulI. Yaron, for the A Collaboration

arXiv · (2020)

Cited 2|Views88
Abstract
We present first measurements of the double ratio of the polarization transfer components (Px/Pz)p/(Px/Pz)s for knock-out protons from s and p shells in 12C measured by the 12C(e,ep) reaction in quasi-elastic kinematics. The data are compared to theoretical predictions in relativistic distorted-wave impulse approximation. Our results show that differences between s- and p-shell protons, observed when compared at the same initial momentum (missing momentum) largely disappear when the comparison is done at the same proton virtuality. We observe no density-dependent medium modifications for protons from s and p shells with the same virtuality in spite of the large differences in the respective nuclear densities.
More
Translated text
PDF
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
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
GPU is busy, summary generation fails
Rerequest