基于神经网络的天文台址长期白日视宁度变化估算  

Estimation of Long-term Daytime Seeing Changes at Observatory Sites Based on Neural Networks

作　　者：胡兴 杨尚斌[1,2] 邓元勇 林佳本[1] 包星明 王全[1,2] HU Xing;YANG Shang-bin;DENG Yuan-yong;LIN Jia-ben;BAO Xing-ming;WANG Quan(National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院国家天文台,北京100101 [2]中国科学院大学,北京100049

出　　处：《天文学报》2024年第4期79-86,共8页Acta Astronomica Sinica

基　　金：国家重点研发计划(2022YFF0503800);国家自然科学基金项目(12250005、12073040);中国科学院青年创新促进会项目(2019059)资助。

摘　　要：视宁度好坏是影响天文观测图像质量的一个决定性因素,目前白日视宁度数据主要是通过太阳差分像运动视宁度检测仪(Solar Differential Image Motion Monitor,SDIMM)或者谱比法获得.由于SDIMM和实际观测所用仪器不同,其测得的视宁度无法反映数据获取时刻的实际视宁度情况,也无法回溯历史既有观测数据对应的大气视宁度.而使用谱比法需要海量短曝光数据,计算成本巨大.基于以上天文观测面临的困难,提出了一种基于神经网络的白日视宁度估算方法,该方法首先对获得的短曝光数据使用谱比法计算对应的视宁度r_(0),构建数据集;然后采用主成分分析的方法对数据进行降维,通过神经网络建立起窄带滤光器太阳光球观测图像和视宁度之间的非线性回归关系,训练集和测试集实验的结果表明该方法可以用于估算视宁度.使用该方法对怀柔观测基地2020年的视宁度进行估算,视宁度中值为2.89 cm,对1989年到2010年连续22 yr的历史观测数据进行长周期的视宁度统计分析,结果表明怀柔基地发布的历史数据对应的视宁度中值在3 cm左右,40%以上数据对应的视宁度超过3 cm,一年中9月份的视宁度最好,该结果验证了怀柔基地视宁度的长期稳定性.此外,该方法也可以从视宁度r_(0)的快速判断出发,为采集到的高质量短曝光图像甄选提供判断依据.The quality of seeing is a decisive factor affecting the image quality of astronomical observations.Currently,daytime seeing data is mainly obtained through the Solar Differential Image Motion Monitor(SDIMM)or spectral ratio method.Due to the non-identity between the SDIMM and the actual observation instrument,it cannot reflect the actual seeing at the time of data acquisition,nor trace the seeing corresponding to historical existing observation data.The spectral ratio method requires a large amount of short-exposure data,the computational cost is huge.Based on the above difficulties faced by astronomical observations,this paper proposes a neural network-based daytime seeing prediction method.This method first uses the spectral ratio method to calculate the corresponding seeing r_(0) for the obtained short-exposure data,and constructs the data.Then,the principal component analysis method was used to reduce the dimensionality of the data,and the nonlinear regression relationship between the observation image of the solar photosphere with the narrow-band filter and the seeing degree was established through the neural network.The experimental results of the training set and the test set show that this method can be used to estimate the seeing.Using this method to estimate the seeing of HSOS(Huairou Solar Observing Station)in 2020,the median visual acuity was 2.89 cm.This method was used to perform the long-term statistical analysis of long-term seeing of historical observation data from 1989 to 2010 for 22 consecutive years,and the results showed that the median visual acuity of HSOS was around 3 cm.Seeing above 3 cm exceeded 40%.The best seeing was observed in September of the year,which confirmed the long-term stability of visual acuity at HSOS.In addition,this method can also provide a judgment basis for selecting high-quality short exposure image frames based on rapid judgment of seeing r_(0).

关 键 词：湍流:视宁度 方法:数据分析 方法:神经网络 

分 类 号：P111[天文地球—天文学]