摘要
Fast radio bursts(FRBs)are among the most studied radio transients in astrophysics,but their origin and radiation mechanism are still unknown.It is a challenge to search for FRB events in a huge amount of observational data with high speed and high accuracy.With the rapid advancement of the FRB research process,FRB searching has changed from archive data mining to either long-term monitoring of the repeating FRBs or all-sky surveys with specialized equipments.Therefore,establishing a highly efficient and high quality FRB search pipeline is the primary task in FRB research.Deep learning techniques provide new ideas for FRB search processing.We have detected radio bursts from FRB 20201124A in the L-band observational data of the Nanshan 26 m radio telescope(NSRT-26m)using the constructed deep learning based search pipeline named dispersed dynamic spectra search(DDSS).Afterwards,we further retrained the deep learning model and applied the DDSS framework to S-band observations.In this paper,we present the FRB observation system and search pipeline using the S-band receiver.We carried out search experiments,and successfully detected the radio bursts from the magnetar SGR J1935+2145and FRB 20220912A.The experimental results show that the search pipeline can complete the search efficiently and output the search results with high accuracy.
基金
supported by the Chinese Academy of Sciences(CAS)“Light of West China”Program(No.2022-XBQNXZ-015)
the National Natural Science Foundation of China(NSFC,grant No.11903071)
the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance(MOF)of China and administered by the Chinese Academy of Sciences(CAS)。
