Tianwen-1 MINPA ion background analysis

Since November 2021, MINPA has continuously performed ion observations, accumulating a substantial dataset for scientific analysis. Although the instrument's background signals are generally weak, they can still interfere with the identification of ion structures and the estimation of plasma parameters under low-signal conditions. To improve data reliability, this study systematically investigates the statistical characteristics of the background signals. The analysis reveals several distinct features, including a uniform energy distribution, strong dependences on azimuthal angle and mass group, and pronounced temporal variability, with notable differences among detection channels. Based on these insights, a dynamic background removal method was developed by leveraging the statistical properties of the background and validated using representative observational events. The results demonstrate that the proposed method effectively suppresses background interference and enhances the extraction of genuine ion signals, notably improving the accuracy of key parameters such as bulk velocity and temperature during low-signal periods. This work establishes a solid foundation for the high-quality utilization of MINPA data and provides methodological support for future investigations of space plasma environments.