Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on PWCNet.This method transforms the transverse velocity field measurements into an optical flow field prediction problem.We evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope observations.The experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning methods.On the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to17.0194.The optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar images.The code implementing the proposed method is available on https://github.com/lygmsy123/transverse-velocity-field-measurement.

Detection and mitigation of RFI in SBRS observation data

In view of the inconsistency of channel gains and a large amount of interference noise in Solar Broadband Radio Spectrometer(SBRS) observation data,they will seriously affect the analysis of SBRS data.In this paper,a method of Radio Frequency Interference(RFI) detection and mitigation for SBRS observation data is reported.Firstly,the SBRS observation data are preprocessed,a part of the observation data was selected to calculate the mean and variance to achieve the normalization of the entire observation data,which can avoid the influence of strong noise on the normalization result.Furthermore,we proposed an adaptive threshold RFI detection method based on fusion wavelet transform reconstruction and an RFI elimination method based on neighborhood weighted filling.It is worth mentioning that to detect RFI interference signals of different magnitudes,we adopted an iterative approach to the RFI detection and mitigation process.Through qualitative analysis of real observation data and quantitative analysis of simulated data,it is shown that the method proposed in this paper can effectively eliminate RFI in SBRS observation data,and improve the quality of observation data for further scientific analysis.