The Effect of Metallicity on the Dynamic Evolution of Globular Cluster
Chinese Astronomy and Astrophysics(2015)
Yunnan Astronomical Observatory
Abstract
The dynamical process and stellar evolution, as well as their mutual interaction will make important influences on the evolution of a globular cluster. As the matallicity may affect the track of stellar evolution, the associated variation of stellar mass loss rate will make influence on the dynamical process of the globular cluster as well. A series of N-body simulations are performed to study the effects of matallicity on the mass loss rate and radius of a globular cluster, and the reasons are analyzed. At the same time, the effects of large-mass stars and the cluster's initial number density distribution are also studied. In the simulations, the adopted initial number of member stars in the globular cluster model is N =50000, they move in the Galaxy-like gravitational potential, and their evolutions are also taken into consideration. The results indicate that because the low-matallicity stars have a faster evolutionary timescale, so in the early period the metal-poor globular cluster will have a higher mass loss, but at the same time its core collapse will be obviously delayed in time, hence after the core collapse its mass loss will be overtaken by the metal-rich globular cluster. In addition, as the evolution of large-mass star causes a larger mass loss, hence the existence of large-mass stars will make the matallicity even significantly influence on the early period expansion of the globular cluster, and the succeeding process of core collapse, besides, the influence of the cluster's initial number density distribution is also not negligible.
MoreTranslated text
Key words
globular clusters,general–stars,evolution–methods,numerical
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
2000
被引用1573 | 浏览
2014
被引用34 | 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
GPU is busy, summary generation fails
Rerequest