The influence of the observational strategies of pulsar timing on the properties of pulsar clocks

作     者：Zhe-Hao Zhang Ming-Lei Tong Cheng-Shi Zhao Xing-Zhi Zhu Feng Gao Zhe-Hao Zhang;Ming-Lei Tong;Cheng-Shi Zhao;Xing-Zhi Zhu;Feng Gao

作者机构：National Time Service CenterChinese Academy of SciencesXi’an 710000China Key Laboratory of Time and Frequency Primary StandardChinese Academy of SciencesXi’an 710600China University of Chinese Academy of SciencesBeijing 100049China Department of Applied PhysicsXi’an University of Science and TechnologyXi’an 710054China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2020年第20卷第12期

页      面：323-329页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.U1831130 and U1531112) the program of Youth Innovation Promotion Association CAS(2017450) 

主　　题：methods:data analysis time pulsars:general 

摘      要：Pulsars are very stable spinning stars, which have the potential to application in the work of time-keeping and autonomous navigation in deep space. For time application, an individual pulsar can be regarded as a clock. The accuracy and stability of a pulsar clock are mainly determined by various timing noises and the measurement errors;however, they would be affected by the concrete observational *** four millisecond pulsars from the first data released by International Pulsar Timing Array(IPTA) as an example, we investigated the influences of different observational strategies on the properties of pulsar clocks by removing some data in various ways. We find that the long-term stabilities of pulsar clocks are nearly not affected by increasing the observational cadence with a fixed time span. It is also found that the capabilities of prediction by pulsar clocks are also hardly affected by different observational strategies,which is reflected by both the stable weighted root-mean-square(wrms) and the stability of the resulting pre-fit timing residuals, unless the data span is too short or the data period is too far from the start of prediction.