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Proton aurora are the most commonly observed yet least studied type of aurora at Mars. In order to better understand the physics and driving processes of Martian proton aurora, we undertake a multi‐model comparison campaign. We compare results from four different proton/hydrogen precipitation models with unique abilities to represent Martian proton aurora: Jolitz model (3‐D Monte Carlo), Kallio model (3‐D Monte Carlo), Bisikalo/Shematovich et al. model (1‐D kinetic Monte Carlo), and Gronoff et al. model (1‐D kinetic). This campaign is divided into two steps: an inter‐model comparison and a data‐model comparison. The inter‐model comparison entails modeling five different representative cases using similar constraints in order to better understand the capabilities and limitations of each of the models. Through this step we find that the two primary variables affecting proton aurora are the incident solar wind particle flux and velocity. In the data‐model comparison, we assess the robustness of each model based on its ability to reproduce a proton aurora observation. All models are able to effectively simulate the general shape of the data. Variations in modeled intensity and peak altitude can be attributed to differences in model capabilities/solving techniques and input assumptions (e.g., cross sections, 3‐D vs. 1‐D solvers, and implementation of the relevant physics and processes). The good match between the observations and multiple models gives a measure of confidence that the appropriate physical processes and their associated parameters have been correctly identified and provides insight into the key physics that should be incorporated in future models. The purpose of the present study is to gain a deeper understanding of the physics and driving processes of Martian proton aurora through a comparative modeling campaign. The models involved in this study have important similarities and differences, such as the dimensionality (e.g., 3‐D vs. 1‐D), inputs, and relevant physics included. We separate the modeling campaign into two steps: a first step comparing the models with each other (i.e., model‐model comparison), and a second step comparing the simulated model results with data from a proton aurora observation (i.e., data‐model comparison) taken by the Imaging UltraViolet Spectrograph onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. We find that all of the models are able to effectively simulate the data in terms of shape and brightness range of the proton aurora observation. The results of this study inform our understanding of the primary influencing factors that cause variability in the Martian proton aurora profile, the effects of dynamically changing solar wind parameters on the coupled Mars‐Sun auroral system, and the physical processes/constraints that should be considered in future modeling attempts of this unique phenomenon. We undertake a multi‐model comparison campaign to gain a better understanding of the physics and driving processes of Martian proton auroraThe incident solar wind particle flux and velocity are found to be the two most influential parameters affecting the proton aurora profileThe models generally reproduce observations, with variations due to different model capabilities/solving techniques and input assumptions We undertake a multi‐model comparison campaign to gain a better understanding of the physics and driving processes of Martian proton aurora The incident solar wind particle flux and velocity are found to be the two most influential parameters affecting the proton aurora profile The models generally reproduce observations, with variations due to different model capabilities/solving techniques and input assumptions

We present fast algorithms to automatically estimate the statistical position of the bow shock from spacecraft data, using existing analytical two‐dimensional (2D) and three‐dimensional (3D) models of the shock surface. We derive expressions of the standoff distances in 2D and 3D and of the normal to the bow shock at any given point on it. Two simple bow shock detection algorithms are constructed, one solely based on a geometrical predictor from existing models, the other using this predicted position to further refine it with the help of magnetometer data, an instrument flown on many planetary missions. Both empirical techniques are applicable to any planetary environment with a defined shock structure. Applied to the Martian environment and the NASA/MAVEN mission, the predicted shock position is on average within 0.15 planetary radius Rpof the bow shock crossing. Using the predictor‐corrector algorithm, this estimate is further refined to within a few minutes of the true crossing (≈0.05Rp). Between 2014 and 2021, we detect 14,929 clear bow shock crossings, predominantly quasi‐perpendicular. Thanks to 2D conic and 3D quadratic fits, we investigate the variability of the shock surface with respect to Mars Years (MY), solar longitude (Ls), and solar EUV flux levels. Although asymmetry in Yand ZMars Solar Orbital coordinates is on average small, we show that for MY32 and MY35, Ls = [135°−225°] and high solar flux, it can become particularly noticeable, and is superimposed to the usual North‐South asymmetry due in part to the presence of crustal magnetic fields. A simple predictor‐corrector algorithm based on magnetic field data is presented to locate the bow shock position in spacecraft dataThe method, biased toward quasi‐perpendicular crossings, is general and applicable to all planetary bodies including Mars, Venus, and EarthMore than 14,900 bow shock crossings are identified with MAVEN for Mars Years 32–35, with 2D/3D fits revealing North‐South asymmetries A simple predictor‐corrector algorithm based on magnetic field data is presented to locate the bow shock position in spacecraft data The method, biased toward quasi‐perpendicular crossings, is general and applicable to all planetary bodies including Mars, Venus, and Earth More than 14,900 bow shock crossings are identified with MAVEN for Mars Years 32–35, with 2D/3D fits revealing North‐South asymmetries

We present an in‐depth analysis of a time interval when quasi‐linear mirror mode structures were detected by magnetic field and plasma measurements as observed by the NASA/Mars Atmosphere and Volatile EvolutioN spacecraft. We employ ion and electron spectrometers in tandem to support the magnetic field measurements and confirm that the signatures are indeed mirror modes. Wedged against the magnetic pile‐up boundary, the low‐frequency signatures last on average ∼10$\sim 10$s with corresponding sizes of the order of 15–30 upstream solar wind proton thermal gyroradii, or 10–20 proton gyroradii in the immediate wake of the quasi‐perpendicular bow shock. Their peak‐to‐peak amplitudes are of the order of 30–35 nT with respect to the background field, and appear as a mixture of dips and peaks, suggesting that they may have been at different stages in their evolution. Situated in a marginally stable plasma with β‖∼ 1, we hypothesize that these so‐called magnetic bottles, containing a relatively higher energy and denser ion population with respect to the background plasma, are formed upstream of the spacecraft behind the quasi‐perpendicular shock. These signatures are very reminiscent of magnetic bottles found at other unmagnetized objects such as Venus and comets, also interpreted as mirror modes. Our case study constitutes the first unmistakable identification and characterization of mirror modes at Mars from the joint points of view of magnetic field, electron and ion measurements. Up until now, the lack of high‐temporal resolution plasma measurements has prevented such an in‐depth study. We detect mirror mode structures at Mars, about 15–30 solar wind thermal proton gyroradii in sizeFor the first time, we use Mars Atmosphere and Volatile EvolutioN magnetic field, ion and electron data to characterize them fullyLocated in the deep magnetosheath, they are likely created behind the quasi‐perpendicular shock We detect mirror mode structures at Mars, about 15–30 solar wind thermal proton gyroradii in size For the first time, we use Mars Atmosphere and Volatile EvolutioN magnetic field, ion and electron data to characterize them fully Located in the deep magnetosheath, they are likely created behind the quasi‐perpendicular shock