Machine Learning-based Separation of the He I 10830Å Chromospheric Signal: Quantitative Analysis of Chromosphere-Corona Intensity in the Quiet Sun

Abstract

The He I 10830Å line, a crucial optically thin chromospheric line, is frequently used to study coronal heating and vertical coupling across the chromosphere-corona interface. However, its images are severely contaminated by the strong photospheric background signal, hindering the analysis of fine chromospheric structures. Given the morphological differences between the Active Region (AR) and the Quiet Sun (QS), we proposed separating the He I 10830Å chromospheric signal using two deep learning CNN models. Our model utilizes TiO images and cross-band learning to infer the He I 10830Å photospheric background. The output is combined with an exponential absorption model to achieve quantitative analysis of the pure chromospheric component. Joint analysis of Solar Dynamics Observatory (SDO) data and the separated QS structures reveals a strong spatial negative correlation between chromospheric He I 10830Å intensities(R approx -0.84 in 304Å ), and significant layered coupling with EUV (171, 193, and 304Å) radiation. Furthermore, strong He I 10830Å absorption areas are highly correlated with regions of strong magnetic fields, while 171Å radiative enhancement areas extend to the strong magnetic field edges and the mixed-polarity regions. These findings quantify the radiation intensity relationship between He I 10830Å and EUV bands in the Quiet Sun. It also demonstrates the differences in heating characteristics between unipolar and mixed-polarity magnetic fields.

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