Fast Radio Bursts(FRBs)have emerged as one of the most intriguing and enigmatic phenomena in the field of radio astronomy.The key of current related research is to obtain enough FRB signals.Computer-aided search is ne...Fast Radio Bursts(FRBs)have emerged as one of the most intriguing and enigmatic phenomena in the field of radio astronomy.The key of current related research is to obtain enough FRB signals.Computer-aided search is necessary for that task.Considering the scarcity of FRB signals and massive observation data,the main challenge is about searching speed,accuracy and recall.in this paper,we propose a new FRB search method based on Commensal Radio Astronomy FAST Survey(CRAFTS)data.The CRAFTS drift survey data provide extensive sky coverage and high sensitivity,which significantly enhance the probability of detecting transient signals like FRBs.The search process is separated into two stages on the knowledge of the FRB signal with the structural isomorphism,while a different deep learning model is adopted in each stage.To evaluate the proposed method,FRB signal data sets based on FAST observation data are developed combining simulation FRB signals and real FRB signals.Compared with the benchmark method,the proposed method F-score achieved 0.951,and the associated recall achieved 0.936.The method has been applied to search for FRB signals in raw FAST data.The code and data sets used in the paper are available at github.com/aoxipo.展开更多
甚长干涉测量技术(Very Long Baseline Interferometry,VLBI)起源于20世纪60年代,它的发展已经对大地测量、地球动力学和天体测量产生了深远的影响。同样,VLBI终端系统作为VLBI系统的重要组成部分,在近40年里也在不断地更新和快速发展,...甚长干涉测量技术(Very Long Baseline Interferometry,VLBI)起源于20世纪60年代,它的发展已经对大地测量、地球动力学和天体测量产生了深远的影响。同样,VLBI终端系统作为VLBI系统的重要组成部分,在近40年里也在不断地更新和快速发展,从一开始的Mark 1系统发展到现在的Mark 5系统,从一开始的磁带记录到现在的硬盘记录甚至通过因特网就能实现数据的实时传输,可以说发生了翻天覆地的变化。如今Mark 6系统也已经开发出来,相信不久的将来,该系统会广泛应用于天文领域。主要描述了VLBI终端系统的发展历程和未来展望。展开更多
文摘Fast Radio Bursts(FRBs)have emerged as one of the most intriguing and enigmatic phenomena in the field of radio astronomy.The key of current related research is to obtain enough FRB signals.Computer-aided search is necessary for that task.Considering the scarcity of FRB signals and massive observation data,the main challenge is about searching speed,accuracy and recall.in this paper,we propose a new FRB search method based on Commensal Radio Astronomy FAST Survey(CRAFTS)data.The CRAFTS drift survey data provide extensive sky coverage and high sensitivity,which significantly enhance the probability of detecting transient signals like FRBs.The search process is separated into two stages on the knowledge of the FRB signal with the structural isomorphism,while a different deep learning model is adopted in each stage.To evaluate the proposed method,FRB signal data sets based on FAST observation data are developed combining simulation FRB signals and real FRB signals.Compared with the benchmark method,the proposed method F-score achieved 0.951,and the associated recall achieved 0.936.The method has been applied to search for FRB signals in raw FAST data.The code and data sets used in the paper are available at github.com/aoxipo.
