  Proton auroras are widely observed on the dayside of Mars, identified as a
significant intensity enhancement in the hydrogen Lyman alpha (121.6 nm)
emission between 110 - 150 km altitudes. Solar wind protons penetrating as
energetic neutral atoms into Mars thermosphere are thought to be primarily
responsible for these auroras. Recent observations of spatially localized
(patchy) proton auroras suggest a possible direct deposition of protons into
Mars atmosphere during unstable solar wind conditions. Improving our
understanding of proton auroras is therefore important for characterizing the
solar wind interaction with Mars atmosphere. Here, we develop a first 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 - 2022. We train an artificial neural network (ANN) that
reproduces individual Lyman alpha intensities and relative Lyman alpha peak
intensity enhancements with a Pearson correlation of 0.94 and 0.60 respectively
for the test data, along with a faithful reconstruction of the shape of the
observed Lyman alpha emission altitude profiles. By performing a SHapley
Additive exPlanations (SHAP) analysis, we find that solar zenith angle, solar
longitude, CO2 atmosphere variability, solar wind speed and temperature are the
most important features for the modeled Lyman alpha peak intensity
enhancements. Additionally, we find that the modeled peak intensity
enhancements are high for early local time hours, particularly near polar
latitudes, as well as weaker induced magnetic fields. Through SHAP analysis, we
also identify the influence of biases in the training data and interdependecies
between the measurements used for the modeling, and an improvement on those
aspects can significantly improve the performance and applicability of the ANN
model.
