An Explainable Deep-learning Model of Proton Auroras on Mars
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".