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Proton auroras are widely observed on the day side of Mars, identified as asignificant intensity enhancement in the hydrogen Ly alpha (121.6 nm) emissionbetween 120 and 150~km altitudes. Solar wind protons penetrating as energeticneutral atoms into the Martian thermosphere are thought to be responsible forthese auroras. Understanding proton auroras is therefore important forcharacterizing the solar wind interaction with the atmosphere of Mars. Recentobservations of spatially localized "patchy" proton auroras suggest a possibledirect deposition of protons into the atmosphere of Mars during unstable solarwind conditions. Here, we develop a purely data-driven model of proton aurorasusing Mars Atmosphere and Volatile EvolutioN (MAVEN) in situ observations andlimb scans of Ly alpha emissions between 2014 and 2022. We train an artificialneural network that reproduces individual Ly alpha intensities with a Pearsoncorrelation of 0.95 along with a faithful reconstruction of the observed Lyalpha emission altitude profiles. By performing a SHapley Additive exPlanations(SHAP) analysis, we find that Solar Zenith Angle, seasonal CO2 atmospherevariability, solar wind temperature, and density are the most importantfeatures for the modelled proton auroras. We also demonstrate that our modelcan serve as an inexpensive tool for simulating and characterizing Ly alpharesponse under a variety of seasonal and upstream solar wind conditions.

An Explainable Deep-learning Model of Proton Auroras on Mars