A Non-Linear Magnetic Field Calibration Method for Filter-Based Magnetographs by Multilayer Perceptron


For filter-based magnetographs, the linear calibration method under the weak-field assumption is usually adopted; this leads to magnetic saturation effect in the regions with strong magnetic field. This article explores a new method to overcome the above disadvantage using a multilayer perceptron network, which we call MagMLP, based on a back-propagation algorithm with one input layer, five hidden layers, and one output layer. We use the data from the Spectropolarimeter (SP) on board Hinode to simulate single-wavelength observations for the model training, and take into account the influence of the Doppler velocity field and the filling factor. The training results show that the linear fitting coefficient (LFC) of the transverse field reaches above 0.91, and that of the longitudinal field is above 0.98. The generalization of the models is good because the corresponding LFCs are above 0.9 for the test subsets. Compared with the linear calibration method, the MagMLP is much more effective on dealing with the magnetic saturation effect. Analyzing an active region, the results of the linear calibration present an evident magnetic saturation effect in the umbra regions; the corresponding systematic error reaches values greater than 1000 G in most areas, or even exceeds 2000 G at some pixels. However, the results of MagMLP at these locations are very close to the inversion results, and the systematic errors are basically within 300 G. In addition, we find that there are many “bright spots” and “dark spots” on the inclination angle images from the inversion results of Hinode/SP with values of 180 and 0 degrees, respectively, where the inversion is not reliable and does not produce a good result; the MagMLP handles these points well.

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We are grateful to the Astronomical Big Data Joint Research Center, co-founded by the National Astronomical Observatories, the Chinese Academy of Sciences, and the Alibaba Cloud. We are thankful to Prof. Song Feng from Kunming University of Science and Technology for providing the code to extract the umbra of solar active region. This project has received funding from the Strategic Priority Research Program on Space Science, the Chinese Academy of Sciences under No. XDA15320300, XDA15320302, XDA15052200, XDA15010800, the National Natural Science Foundation of China (NSFC) under No.11873027, 11773072, 11427803, 11427901, 11773040, 11573012, 11833010, 11973056, 11873062, 11703042, and Beijing Municipal Science and Technology under No. Z181100002918004.

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Guo, J., Bai, X., Deng, Y. et al. A Non-Linear Magnetic Field Calibration Method for Filter-Based Magnetographs by Multilayer Perceptron. Sol Phys 295, 5 (2020). https://doi.org/10.1007/s11207-019-1573-9

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  • Magnetic fields
  • Calibration
  • Machine learning
  • Multilayer perceptron