基于图像相减和随机森林的AST3巡天暂现源及变源搜寻方法
作者:
作者单位:

1.中国科学院紫金山天文台南京210033;2.中国科学技术大学天文与空间科学学院合肥230026;3.George P. and Cynthia Woods Mitchell Institute for Fundamental Physics & Astronomy, Texas A. & M. University, Department of Physics and Astronomy, Texas TX 77843;4.中国南极天文中心南京210033;5.清华大学物理系/清华天体物理中心北京100084; 6.The Observatories of the Carnegie Institution for Science, California CA 91101;7.Department of Astrophysics, University of New South Wales, New South Wales NSW 2052)

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金项目(11725314、11673068、11325313、11633002、11761141001),中国科学院前沿科学重点研究项目(QYZDB-SSW-SYS005), 中国科学院战略性先导科技专项(XDB2300000), 江苏省第5期``333工程'培养资金项目资助


An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest
Author:
Affiliation:

1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;Department of Astronomy, University of Science and Technology of China, Hefei 230026;3. George P. and Cynthia Woods Mitchell Institute for Fundamental Physics & Astronomy, Texas A. & M. University, Department of Physics and Astronomy, Texas TX 77843;4. Chinese Center for Antarctic Astronomy, Nanjing 210033;5. Physics Department and Tsinghua Center for Astrophysics, Tsinghua University, Beijing 100084;6. The Observatories of the Carnegie Institution for Science, California CA 91101;7. Department of Astrophysics, University of New South Wales, New South Wales NSW 2052;

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    AST3-2 (Antarctic Survey Telescopes)光学巡天望远镜位于南极大陆最高点冰穹A, 其产生的大量观测数据对数据处理的效率提出了较高要求. 同时南极通信不便, 数据回传有诸多困难, 有必要在南极本地实现自动处理AST3-2观测数据, 进行变源和暂现源观测的数据处理, 但是受到低功耗计算机的限制, 数据的快速自动处理的实现存在诸多困难. 将已有的图像相减方案同机器学习算法相结合, 并利用AST3-2 2016年观测数据作为测试样本, 发展一套的暂现源及变源的筛选方法成为可行的选择. 该筛选方法使用图像相减法初步筛选出可能的变源, 再用主成分分析法抽取候选源的特征, 并选择随机森林作为机器学习分类器, 在测试中对正样本的召回率达到了97%, 验证了这种方法的可行性, 并最终在2016年观测数据中探测出一批变星候选体.

    Abstract:

    AST3-2 (Antarctic Survey Telescopes) Telescope locates in Dome A, the loftiest ice dome on the Antarctic Plateau. It produces huge amount of observation data which requires more efficient data reduction program to be developed. Also data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction to detect variable sources and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the poor computer performance in Antarcitca.For the realization of this aim, developing a new method based on pre-existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as test sample becomes an alternative choice. This method performs image subtraction on data set, then applies principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and a recall rate of 97% is resulted. Our work verifies the feasibility and accuracy of our method, and finally finds out a batch of candidates for variable stars in the AST3-2 2016 dataset.

    参考文献
    相似文献
    引证文献
引用本文

黄天君,孙天瑞,胡镭,宁宗军,吴雪峰,王力帆,王晓峰,朱镇熹,UDDIN Ashraf Syed,ASHLEY Charles Brewster Michael.基于图像相减和随机森林的AST3巡天暂现源及变源搜寻方法[J].天文学报,2019,60(5):98-114.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2019-04-14
  • 最后修改日期:2019-04-24
  • 录用日期:
  • 在线发布日期: 2019-10-16
  • 出版日期: