Identifying Typical Relativistic Electron Pitch Angle Distributions: Evolution During Geomagnetic Storms
Bibliographic record
Abstract
Abstract Van Allen radiation belt electron dynamics are governed by a multitude of physical processes that can simultaneously drive acceleration, transport and loss. However, each individual process can be linked to a specific energy‐dependent pitch angle distribution (PAD). We employ a new, unsupervised machine learning technique on 7‐year of Van Allen Probe Relativistic Electron‐Proton Telescope data and discover that six PADs successfully describe 93% of outer belt relativistic electrons, two each of: pancake, butterfly, and flattop. We investigate the occurrence and storm‐time evolution of each PAD through 45 geomagnetic storms. We find new populations of PADs, including: “shadowing‐like” and wave‐particle interaction signatures at low‐L, and radial diffusion and substorm injections at higher‐L, as well as determining that wave‐particle interaction dominated PADs are swamped by radial diffusion processes through geomagnetic storms. Our results clearly demonstrate that PAD characterization is a key component of understanding Van Allen radiation belt electron dynamics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".