An Explainable Deep-learning Model of Proton Auroras on Mars
Dattaraj B. Dhuri Dimitra Atri Ahmed AlHantoobi
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.