钛空腔靶高强度keV X射线源的实验研究
High Power Laser and Particle Beams(2015)
Abstract
利用神光-Ⅲ原型激光装置实验研究了8束ns激光脉冲从一端注入钛空腔靶产生的keV X射线源的辐射特征,发现keV X射线主要产生于腔轴附近;钛空腔靶内径过大时keV X射线能流的峰值强度较低,内径过小时keV X射线能流的持续时间较短.为了在4π空间内使钛空腔靶获得最大的X射线(4~7 keV能段)转换效率,腔内径的最优值在1000~1300 μm附近,此时的keV X射线转换效率为4.7%,是相同激光参数下钛平面靶的2倍左右.激光单端注入有底部钛膜和无底部钛膜的空腔靶对比实验显示,底膜能够增强keV X射线的发射.
MoreKey words
X-ray source,laser,hohlraums,conversion efficiency
求助PDF
上传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
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