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
无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模...无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模型的关系。实验结果表明,如果信道是高斯噪声信道,可采用国标法进行频率占用度测量;如果信道是非高斯噪声信道,采用5西格玛原则统计频率占用度更好。采用机器学习方法研究了无线信道占用状况,结果表明,信噪比(Signal to Noise Ratio,SNR)大于15 dB时,基于信号频谱分类模型的5类无线电信号的分类准确率可达到99%以上;在低SNR和非高斯噪声信道下如何进一步提高无线电信号的分类准确率是一个挑战。展开更多
利用毫米波射电天文所(Institut de Radioastronomie Millimétrique,IRAM)30 m射电望远镜在1.3–4.0 mm波段的分子谱线成图,结合70–870μm连续谱数据以及绿岸射电望远镜(Green Bank Telescope,GBT)对NH_(3)(J,K)=(1,1)和(2,2)谱...利用毫米波射电天文所(Institut de Radioastronomie Millimétrique,IRAM)30 m射电望远镜在1.3–4.0 mm波段的分子谱线成图,结合70–870μm连续谱数据以及绿岸射电望远镜(Green Bank Telescope,GBT)对NH_(3)(J,K)=(1,1)和(2,2)谱线的成图(J为总角动量量子数,而K描述角动量在分子主轴方向的分量),对G 011.0970-0.1093纤维状分子云末端的一对比邻云团进行了观测.尽管在870μm波段下它们都表现出与连续谱流量峰值成协,但在70μm波段下,它们呈现出明亮与暗弱的对照特性.对这两个云团进行对比分析发现:(1)该极早期大质量恒星形成区的气体尘埃温度高度耦合;(2)70μm明亮云团的气体尘埃温度从中心(约17 K)到包层边缘呈递减趋势,这预示着云团内部可能已经有原恒星形成;70μm暗弱云团中心较边缘更冷(约11K)更致密(约5×10^(22)cm^(-2)),且氢分子柱密度与尘埃温度存在强反相关,故外部辐射可能主导该云团加热;(3)在pc尺度上,气态C^(18)O的耗损率f_(D)(C^(18)O)与HCO^(+)的氘化率存在强正相关,且气态C^(18)O在冷且致密的DCO^(+)主导云团耗损f_(D)(C^(18)O)高达7;(4)在气体尘埃温度从10 K到20 K,氢分子柱密度从10^(22)cm^(-2)到10^(23)cm^(-2)的变化环境里,HCO^(+)、N_(2)H^(+)、HNC在更.为致密冷暗的环境氘化率增丰明显,HCN则在稍温暖且70μm明亮的环境氘化率增丰更大,而NH3氘化率在此环境中没有明显变化.这些不同分子氘化增丰的差异性可能源自其气体尘埃反应路径的差异性.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.
文摘无线电频率占用度是表征无线电频谱真实占用状况的主要指标。针对目前国家规范测量无线电频率占用度以及频率占用度与无线信道状态关联研究存在的问题,建立了无线电信号发射与频谱接收系统,研究了无线电频率占用度测量方法与信道噪声模型的关系。实验结果表明,如果信道是高斯噪声信道,可采用国标法进行频率占用度测量;如果信道是非高斯噪声信道,采用5西格玛原则统计频率占用度更好。采用机器学习方法研究了无线信道占用状况,结果表明,信噪比(Signal to Noise Ratio,SNR)大于15 dB时,基于信号频谱分类模型的5类无线电信号的分类准确率可达到99%以上;在低SNR和非高斯噪声信道下如何进一步提高无线电信号的分类准确率是一个挑战。
