基于深层残差网络的脉冲星候选体分类方法研究

随着下一代射电天文望远镜的不断改进和发展,脉冲星巡天观测将发现数百万个脉冲星候选体,这给脉冲星的识别和新脉冲星的发现带来了巨大挑战,迅速发展的人工智能技术可用于脉冲星识别.使用Parkes望远镜的脉冲星数据集(The High Time Resolution Universe Survey,HTRUS),设计了一个14层深的残差网络(Residual Network,ResNet)进行脉冲星候选体分类.在HTRUS数据样本中,存在非脉冲星候选体(负样本)的数目远远大于脉冲星候选体(正样本)数目的样本非均衡问题,容易产生模型误判.通过使用过采样技术对训练集中的正样本进行数据增强,并调整正负样本的比例,解决了正负样本非均衡问题.训练过程中,使用5折交叉验证来调节超参数,最终构建出模型.测试结果表明,该模型能够取得较高的精确度(Precision)和召回率(Recall),分别为98%和100%,F1分数(F1-score)能够达到99%,每个样本检测完成只需要7 ms,为未来脉冲星大数据分析提供了一个可行的办法.

Research on Pulsar Candidate Identification Method Based on Deep Residual Neural Network

As the next generation of radio astronomical telescopes continue to improve and develop, the pulsar survey will produce millions of pulsar candidates, which pose considerable challenges for pulsar identification and classification. The rapidly evolving artificial intelligence (AI) techniques are being used for pulsar identification and discovery of new pulsars. Using the pulsar data set observed with the Parkes telescope, the High Time Resolution Universe Survey (HTRUS), a 14-layer deep residual network was designed (called Residual Network, ResNet) for pulsar candidate classification. In the HTRUS sample, the number of non-pulsar candidates (i.e., negative sample) is much larger than that of pulsar candidates (i.e., positive sample). The imbalance of positive and negative samples is prone to result in model misinterpretation. By using the oversampling technique to enhance the data of positive samples in the training set and adjust the ratio of positive and negative samples, we solve this Non-equilibrium problem. During training, the hyperparameters were adjusted using 5-fold cross-validation to construct the model. The test results show that the model can achieve high precision (98%) and recall (100%), respectively, and the F1-score is able to reach 99% for each sample tested. It takes only 7 ms to complete each candidate classification, providing a viable approach to future pulsar big data analysis.