An Explainable Deep-learning Model of Proton Auroras on Mars

Proton auroras are widely observed on the day side of Mars, identified as a significant intensity enhancement in the hydrogen Ly alpha (121.6 nm) emission between 120 and 150~km altitudes. Solar wind protons penetrating as energetic neutral atoms into the Martian thermosphere are thought to be responsible for these auroras. Understanding proton auroras is therefore important for characterizing the solar wind interaction with the atmosphere of Mars. Recent observations of spatially localized "patchy" proton auroras suggest a possible direct deposition of protons into the atmosphere of Mars during unstable solar wind conditions. Here, we develop a purely data-driven model of proton auroras using Mars Atmosphere and Volatile EvolutioN (MAVEN) in situ observations and limb scans of Ly alpha emissions between 2014 and 2022. We train an artificial neural network that reproduces individual Ly alpha intensities with a Pearson correlation of 0.95 along with a faithful reconstruction of the observed Ly alpha emission altitude profiles. By performing a SHapley Additive exPlanations (SHAP) analysis, we find that Solar Zenith Angle, seasonal CO2 atmosphere variability, solar wind temperature, and density are the most important features for the modelled proton auroras. We also demonstrate that our model can serve as an inexpensive tool for simulating and characterizing Ly alpha response under a variety of seasonal and upstream solar wind conditions.