WeChat Mini Program
Old Version Features

A Model for Calculating the Abundances of Neutron-Capture Elements in Metal-Poor Stars

ASTROPHYSICAL JOURNAL(1999)

Peking Univ

Cited 15|Views14
Abstract
Several previous studies on the abundances of neutron-capture elements have indicated that, for most metal-poor stars, the observed abundances of the heavy elements cannot be matched by only one neutron-capture process, either the solar system r-process or the solar s-process abundances. However, the observed abundances can be well matched by the combined contributions from both of these solar system neutron-capture processes in certain proportions. So it is necessary to determine the relative contributions from the individual neutron-capture processes to the abundances of the heavy elements in metal-poor stars. In this paper we suggest a new concept of component coefficients to describe the relative contributions of the individual n-processes to the synthesis of the heavy elements and we set up a model to calculate the component coefficients and the abundances of heavy elements in metal-poor stars with different metallicities. With this model, we then calculate the component coefficients and the abundances of the heavy elements in 18 metal-poor stars. We find that, for most sample stars, the model calculations are basically in agreement with the observations of the heavy elemental abundances within the error limits and the fits of the model predictions are much better for the heaviest elements than for the lighter elements, specifically for Sr, Y, and Zr. We discuss this result and give a possible explanation for it. Moreover, we also discuss the physical meanings of the component coefficients.
More
Translated text
Key words
nuclear reactions, nucleosynthesis, abundances,stars : Population II
上传PDF
Bibtex
收藏
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
Upload PDF to Generate Summary
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
Summary is being generated by the instructions you defined