基于机器学习的单脉冲搜索候选体识别对FAST观测CRAFTS数据的应用研究

Application of Single-Pulse Search Candidate Identi cation Based on Machine Learning to FAST Observation CRAFTS Data

单脉冲搜索作为脉冲星探测的有力工具,在探测旋转射电暂现源以及快速射电暴中扮演着重要角色。为了从海量的射电巡天数据中快速筛选出最有价值的单脉冲搜索候选体,候选体识别已经从早期启发式阈值判断发展到基于机器学习自动识别。对于FAST观测,研究了基于机器学习的单脉冲搜索候选体识别应用到CRAFTS(the commensal radio astronomy FAST survey)超宽带脉冲星数据的性能表现。在评估过程中,使用单脉冲事件组识别(SPEGID)和单脉冲搜索器(SPS)两类自动识别方法,通过7种不同机器学习分类器对CRAFTS基准数据集产生的单脉冲搜索候选体进行自动识别;作为对比,也使用了启发式阈值判断的方法(RRATtrap和Clusterrank)。结果表明,SPEGID具有最好的性能表现(最高的F1-score值95.1%、次高的召回率95.4%、最低的假阳性率4.7%),SPS具有最快的筛选速度(平均每小时筛选4010个候选体)。通过对比分析结果,探讨了如何基于FAST观测数据开展高效的单脉冲搜索候选体识别。

As a powerful tool for pulsar detection,single-pulse search plays an important role in detecting rotating radio transient sources and fast radio bursts.In order to quickly screen out the most valuable single-pulse search candidates from massive radio survey data,candidate identification has developed from early heuristic threshold judgment to automatic identification based on machine learning.For FAST observations,the performance of machine learning-based single-pulse search candidate identification applied to the commensal radio astronomy FAST survey(CRAFTS)ultra-wideband pulsar data was studied.In the evaluation process,two automatic recognition methods,single pulse event group recognition(SPEGID)and single pulse search device(SPS),were used to automatically identify the single-pulse search candidates generated by the CRAFTS benchmark dataset through seven different machine learning classifiers.For comparison,heuristic threshold judgment methods(RRATtrap and Clusterrank)are also used.The results showed that SPEGID had the best performance(highest F1-score 95.1%,next highest recall 95.4%,lowest false positive rate 4.7%),and SPS had the fastest screening speed(an average of 4010 candidates per hour).By comparing the results of the analysis,how to carry out efficient work based on FAST observation data is discussed single-pulse search candidate identification.