A Robust RFI Identification Method for Radio Interferometry Based on LightGBM
A Robust RFI Identification Method for Radio Interferometry Based on LightGBM
复制标题
基于LightGBM的射电干涉鲁棒RFI识别方法
DOI：
10.1088/1538-3873/acab2e
复制
发表时间：
2022
影响因子：
3.5
通讯作者：
Wang Feng
中科院分区：
文献类型：
--
作者：
Li Weijie;Cao Zhong;Deng Hui;Mei Ying;Chen Linjie;Wang Feng
射频干扰是影响射电望远镜观测数据质量的重要因素。在构建平方公里阵列（SKA）射电干涉仪中，有效地处理射频干扰（RFI）一直是数据处理中的热点问题。传统的识别方法存在查准率和查全率差的问题，现有的基于机器学习的识别方法模型复杂，处理效率低。我们提出了一个LightGBM识别方法的基础上以前的机器学习研究识别RFI。基于SKA 1-LOW模拟观测数据，我们构建了五个可见度函数数据集，一个用于建模，其余用于验证。实验结果表明，F2分数达到0.9583，训练和预测速度比最近类似研究中的卷积神经网络要快得多。然后，我们进一步研究了该模型在识别RFI从实际MeerKAT观测的有效性。结果表明，整体效果与Tfcrop和Rflag等工具相当，在识别速度上优于现有方法。
Radio frequency interference is an essential factor affecting the observation data quality of radio telescopes. In constructing the Square Kilometer Array (SKA) radio interferometer, dealing with radio frequency interference (RFI) effectively is always a hot issue in data processing. Traditional identification methods have poor precision or recall, and existing machine-learning-based methods have complicated models and low processing efficiency. We propose a LightGBM identification method based on previous machine-learning research to identify RFI. Based on the data of SKA1-LOW simulation observations, we construct five visibility function data sets, one for modeling and the rest for validation. The experimental results show that the F 2-score reaches 0.9583, and the training and prediction speed are much more efficient than those of convolutional neural networks in a similar recent study. Then, we further investigate the effectiveness of this model in identifying RFI from actual MeerKAT observations. The results show that the overall effectiveness is comparable to tools such as Tfcrop and Rflag, improving over existing methods in identification speed.