中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 云南天文台
  3. 中国科学院云南天文台
  4. 中国科学院天体结构与演化重点实验室
Pulsar candidate identification using advanced transformer-based models

文献类型：期刊论文

作者Cao, Jie2; Xu, Tingting2; Deng, Linhua2; Zhou, Xueliang2; Li, Shangxi2; Liu, Yuxia2; Zhou, Weihong1,2
刊名CHINESE JOURNAL OF PHYSICS
出版日期2024-08
卷号90页码:121-133
关键词Pulsars General Methods Data analysis Techniques Image processing
ISSN号0577-9073
DOI10.1016/j.cjph.2024.05.020
文献子类Article
英文摘要Rapid and accurate identification of pulsars is a significant topic for large radio telescope surveys. With the enhancement of astronomical instruments, modern radio telescopes are witnessing an exponential increase in pulsar candidate detections. The application of artificial intelligence for the identification of pulsar candidates is an automated and highly effective solution to tackle the challenge of processing and recognizing vast volumes of data. In this work, using the data released by two surveys, the Commensal Radio Astronomy FasT Survey (CRAFTS) and High -Time Resolution Universe (HTRU), we propose a new framework to identify pulsar candidates. Firstly, due to the small number of real pulsars, we compare the performance of different data augmentation methods and find that the pulsar samples generated by the Deep Convolutional Generative Adversarial Network (DCGAN) based on deep learning techniques are closer to real pulsars. Secondly, we use two transformer -based classification models, Vision Transformer (ViT) and Convolutional Vision Transformer (CvT), to classify pulsar candidates, and find that the evaluation indexes of pulsar candidate classification based on two transformers can reach 100%. Finally, we use the t -distributed Stochastic Neighbor Embedding (t-SNE) algorithm to visualize the results of our identification framework. The results showed that pulsar and non -pulsar samples are separated from each other in multidimensional space. Therefore, it is a new attempt to apply transformer technology to pulsar candidate classification, and it could be of great significance to subsequent theoretical research.
学科主题天文学 ; 射电天文学
URL标识查看原文
出版地RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
WOS关键词CLASSIFICATION ; DISCOVERY
资助项目National Nature Science Foundation of China[61561053]; Yunnan Fundamental Research Projects, China[202301AV070007]; Yunnan Fundamental Research Projects, China[202401AU070026]; Yunnan Revitalization Talent Support Program Innovation Team Project, China[202405AS350012]; Scientific Research Foundation Project of Yunnan Education Department, China[2023J0624]; Scientific Research Foundation Project of Yunnan Education Department, China[2024Y469]
WOS研究方向Physics
语种英语
WOS记录号WOS:001246487300001
出版者ELSEVIER
资助机构National Nature Science Foundation of China[61561053] ; Yunnan Fundamental Research Projects, China[202301AV070007, 202401AU070026] ; Yunnan Revitalization Talent Support Program Innovation Team Project, China[202405AS350012] ; Scientific Research Foundation Project of Yunnan Education Department, China[2023J0624, 2024Y469]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27396]  
专题云南天文台_中国科学院天体结构与演化重点实验室
作者单位1.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy China of Sciences, Kunming 650011, China
2.School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China;
推荐引用方式
GB/T 7714		 Cao, Jie,Xu, Tingting,Deng, Linhua,et al. Pulsar candidate identification using advanced transformer-based models[J]. CHINESE JOURNAL OF PHYSICS,2024,90:121-133.
APA Cao, Jie.,Xu, Tingting.,Deng, Linhua.,Zhou, Xueliang.,Li, Shangxi.,...&Zhou, Weihong.(2024).Pulsar candidate identification using advanced transformer-based models.CHINESE JOURNAL OF PHYSICS,90,121-133.
MLA Cao, Jie,et al."Pulsar candidate identification using advanced transformer-based models".CHINESE JOURNAL OF PHYSICS 90(2024):121-133.

入库方式： OAI收割

来源：云南天文台

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。