基于神经网络的双折射窄带滤光器型磁像仪观测波长点的定标  

Calibration of Observing Wavelength Points of Birefringent Narrow Band Filter-Type Magnetograph Based on Neural Network

作　　者：胡兴 杨尚斌[1,3,4] 季凯帆 林佳本[1,3,4] 邓元勇 白先勇[1,3,4] 朱晓明 白阳[1,3] 王全 Hu Xing;Yang Shangbin;Ji Kaifan;Lin Jiaben;Deng Yuanyong;Bai Xianyong;Zhu Xiaoming;Bai Yang;Wang Quan(National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;Yunnan Observatories,Chinese Academy of Sciences,Kunming 650217,Yunnan,China;Key Laboratory of Solar Activity,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家天文台,北京100101 [2]中国科学院云南天文台,云南昆明650217 [3]中国科学院太阳活动重点实验室,北京100101 [4]中国科学院大学,北京100049

出　　处：《中国激光》2023年第13期84-93,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(11427901,12073040);国家重点研发计划(2022YFF0503800,2021YFA1600500);中国科学院空间科学战略性先导科技专项(XDA15320102,XDA15320302,XDA15010700);中科院青促会项目(2019059)。

摘　　要：滤光器型磁像仪在固定波长点观测时,受到温度变化、机械误差等因素影响,观测波长点发生偏移。传统的波长点定标方法通过拟合谱线轮廓来对观测点进行定标,耗时多且无法实时校正观测波长点。为此提出一种基于神经网络的观测波长点的高效定标方法。该方法首先通过分析不同波长点处的图像特征差异,设计一套有效的数据预处理方案;然后通过机器学习下的神经网络建立起实时观测图像与对应观测波长点的非线性关系。方法验证和实际测试的结果表明该方法比现有的方法快100多倍,同时可监测仪器运行状态。最后,针对磁像仪系统频繁维修后需重新训练网络的问题,给出克服系统变化的方案。该方法可实现滤光器位置实时定标,有效减少定标过程中电机频繁旋转带来的滤光器工作寿命缩短现象,提高地面和空间太阳磁场观测的效率和稳定性。Objective The filter-type magnetograph is one of the main devices for measuring the solar vector magnetic field.SMAT in Huairou,a solar magnetograph,is initially used for conventional observation in China.It obtains polarization information at a fixed temperature and wavelength point,and then acquires the solar vector magnetic field through the calibration process.Due to the changeable factors such as temperature variation and mechanical errors(e.g.,tooth gap),the wavelength points observed by the filter would be altered,which weakens or removes the polarization signal.It would finally affect the accuracy of solar vector magnetic field measurement.The current method of wavelength point calibration takes more time,less data and lower temporal resolution by scanning the spectral line profile and locating wavelength points.In addition,the frequent mechanical rotation lowers the lifetime of filter,which further impedes the acquisition of stable and high-quality data.Last but not least,the current method could not form a real-time and closed-loop system to distinguish and control the wavelength points.In view of this,based on the analysis of the data characteristic of SMAT,we summarize a new data pre-processing way,employ the supervised learning of machine learning and then propose a neural-network-based observation scheme of wavelength point calibration.This scheme has established the relationship between a single frame image and the corresponding wavelength point,which shortens the time of locating the position of wavelength point by a single frame image.Methods The present study uses the spectral line scan data from SMAT,which are 31 monochromatic images obtained by moving the filter from the blue to the red side of the spectral line,subject to the observation conditions.We first analyze the data characteristics.It is found that the Doppler velocity generated by the rotation of the Sun from west to east causes the image to exhibit a large scale uneven distribution of grayscale(brighter on one side and darker on the

关 键 词：测量 滤光器型磁像仪 波长点定标 预处理 机器学习 

分 类 号：P111.2[天文地球—天文学]