Modeling the past and future activity of the Halleyid meteor showers
Bibliographic record
Abstract
Context.We present a new numerical model of theη-Aquariid and Orionid meteor showers. Aims.The model investigates the origin, variability, and age of theη-Aquariid and Orionid apparitions from 1985 to the present day in order to forecast their activity over the next several decades. Methods.Through the numerical integration of millions of simulated meteoroids and a custom-made particle weighting scheme, we model the characteristics of everyη-Aquariid and Orionid apparition between 1985 and 2050. The modeled showers are calibrated using 35 yr of meteor observations, including the shower activity profiles and interannual variability. Results.Our model reproduces the general characteristics of the present-dayη-Aquariids and part of the Orionid activity. Simulations suggest that the age of theη-Aquariids somewhat exceeds 5000 yr, while a greater fraction of the Orionids is composed of older material. The 1:6 mean motion resonance with Jupiter plays a major role in generating some (but not all) Halleyid stream outbursts. We find consistent evidence for a periodicity of 11.8 yr in both the observations and modeled maximum meteor rates for the Orionids. Weaker evidence of a 10.7 yr period in the peak activity for theη-Aquariids needs to be investigated with future meteor observations. The extension of our model to future years predicts no significant Orionid outbursts through 2050 and four significantη-Aquariid outbursts, in 2023, 2024, 2045, and 2046.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".