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 observationa...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.展开更多
Fast radio bursts(FRBs)are radio transients that are bright and have short duration,with their physical mechanism not being fully understood.We conducted a targeted search for bursts from FRB 20201124A between 2021 Ju...Fast radio bursts(FRBs)are radio transients that are bright and have short duration,with their physical mechanism not being fully understood.We conducted a targeted search for bursts from FRB 20201124A between 2021 June 2and July 20.High time-resolution data were collected for 104.5 hr using the ROACH2-based digital backend.We introduce the details of our FRB search pipeline which is based on HEIMDALL and FETCH.Testing of the injected mock FRBs search could help us better understand the performance of the pipelines,and improve the search algorithms and classifiers.To study the efficiency of our pipeline,5000 mock FRBs were injected into the data and searched using the pipeline.The results of the mock FRB search show that our pipeline can recover almost all(?90%)the injected mock FRBs above a signal-to-noise ratio(S/N)threshold of 15,and the performance is still acceptable(?80%)for injected S/Ns from 10 to 15.The recovery fraction displays relations with S/N,dispersion measure and pulse width.No bursts were detected from FRB 20201124A in the middle of 2021.The non-detection of FRB 20201124A may be due to its quiet phase window or no emission above the threshold of the Nanshan telescope.展开更多
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)。
文摘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 National Natural Science Foundation of China(NSFC,Grant Nos.11873080,12041304,U1838109 and 12041301)the CAS Jianzhihua project+3 种基金supported by the Key Laboratory of Xinjiang Uygur Autonomous Region No.2020D04049the National SKA Program of China No.2020SKA0120200the 2018 Project of Xinjiang Uygur Autonomous Region of China for Flexibly Fetching Upscale Talentspartly supported by the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)。
文摘Fast radio bursts(FRBs)are radio transients that are bright and have short duration,with their physical mechanism not being fully understood.We conducted a targeted search for bursts from FRB 20201124A between 2021 June 2and July 20.High time-resolution data were collected for 104.5 hr using the ROACH2-based digital backend.We introduce the details of our FRB search pipeline which is based on HEIMDALL and FETCH.Testing of the injected mock FRBs search could help us better understand the performance of the pipelines,and improve the search algorithms and classifiers.To study the efficiency of our pipeline,5000 mock FRBs were injected into the data and searched using the pipeline.The results of the mock FRB search show that our pipeline can recover almost all(?90%)the injected mock FRBs above a signal-to-noise ratio(S/N)threshold of 15,and the performance is still acceptable(?80%)for injected S/Ns from 10 to 15.The recovery fraction displays relations with S/N,dispersion measure and pulse width.No bursts were detected from FRB 20201124A in the middle of 2021.The non-detection of FRB 20201124A may be due to its quiet phase window or no emission above the threshold of the Nanshan telescope.
