An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest |
| |
| Institution: | 1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;2. 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 Qinghua Center for Astrophysics, Qinghua 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;1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;2. Key Laboratory of Planetary Sciences, Chinese Academy of Sciences, Nanjing 210033;3. School of Astronomy and Space Science, Nanjing University, Nanjing 210023;1. Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210023;2. School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044;3. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;1. Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210008;2. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;1. Key Laboratory foe Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, Nanjing 210033;2. University of Chinese Academy of Sciences, Beijing 100049;3. School of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026;1. School of Physics and Electronic Information Technology, Yunnan Normal University, Kunming 650500;2. Key Laboratory of High Energy Astrophysics of University of Yunnan Province, Kunming 650500 |
| |
| Abstract: | AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset. |
| |
| Keywords: | Stars: variables: general—methods: data analysis—techniques: image processing |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
