Removal of background white light noise in coherent-dispersion spectrometer based on spatial-frequency dual-domain

As an important method for exoplanet detection, the key to the radial velocity (RV) method lies in the high-precision measurement of stellar RV. Coherent-dispersion spectrometer (CODES), which obtains the RV by analyzing the Doppler phase shift of stellar absorption line interference spectrum, shows significant potential in the application of the RV method due to its high energy utilization efficiency and excellent environmental stability. However, the background white light in the stellar absorption spectrum makes phase noise to the CODES, which leads to a significant decrease and fluctuation in the RV inversion precision. To remove the phase noise caused by background white light, the Dual-domain background white light prediction network (DDBWP-Net) is proposed by integrating spatial and frequency domain processing. In DDBWP-Net, firstly, the main energy of the background white light and the absorption line is separated through frequency-domain processing, and then the global high-frequency and low-frequency features of the background white light interference spectrum are extracted. Secondly, the global high-frequency and low-frequency features are fused deeply by introducing the channel attention mechanism, enhancing the integrity of the feature representation. Thirdly, the multi-scale local detail features of the background white light interference spectrum are extracted by utilizing the spatial convolution based on the double-layer residual structure. Finally, the prediction result of the background white light interference spectrum is reconstructed. The experimental results show that the RV inversion error is mainly distributed in the range of 0-0.15 m/s, with the mean error of 0.08 m/s and the root mean square error of 0.13 m/s, after removing the background white light predicted by DDBWP-Net from the original absorption line interference spectrum. Moreover, with different absorption lines and different fixed optical path differences, the error distribution shows good uniformity. This indicates that DDBWP-Net can predict background white light accurately, and has strong stability and robustness, providing solid technical support for CODES to achieve high-precision exoplanet detection.