An Integrated Auto‐Diagnosis Method for MAVEN SWEA Measurements: Validation and Statistical Analysis

This study develops and examines an integrated automated method for identifying Martian photoelectrons, suprathermal electron void and Photoelectron Boundary (PEB) using measurements from the Mars Atmospheric and Volatile EvolutioN (MAVEN) Solar Wind Electron Analyzer (SWEA). The core of the approach is grounded by a robust Support Vector Machine (SVM) classification model, trained on a large manually vetted data set of photoelectron (PHE) and non‐photoelectron (nPHE) spectra. To eliminate sporadic misclassifications inherent in the SVM classifier, particularly near the PEB or under nightside conditions, a rigorous calibration routine is implemented. Applying this method facilitates a statistical analysis of photoelectron, electron voids, and PEB characteristics. The results are consistent with the existing literature on the occurrence feature modulated by solar zenith angle, altitude, and crustal magnetic field strength, thereby enhancing the credibility of the method. Comparative analysis further demonstrates the advantages of this approach over previous studies by resolving ambiguities and presenting several novel occurrence features, which will shed light on our understanding of the Martian ionosphere.