Not So Fast: A New Catalog of Meteor Persistent Trains
Bibliographic record
Abstract
Abstract This paper presents the results of a nearly 2‐year long campaign to detect and analyze meteor persistent trains (PTs)—self‐emitting phenomena which can linger up to an hour after their parent meteor. The modern understanding of PTs has been primarily developed from the Leonid storms at the turn of the century; our goal was to assess the validity of these conclusions using a diverse sample of meteors with a wide range of velocities and magnitudes. To this end, year‐round observations were recorded by the Widefield Persistent Train camera, 2nd edition (WiPT2) and were passed through a pipeline to filter out airplanes and flag potential meteors. These were classified by visual inspection based on the presence and duration of trains. Observed meteors were cross‐referenced with the Global Meteor Network (GMN) database, which independently detects and calculates meteor parameters, enabling statistical analysis of PT‐leaving meteors. There were 4,726 meteors codetected by the GMN, with 636 of these leaving trains. Among these were a large population of slow, dim meteors that left PTs; these slower meteors had a greater train production rate relative to their faster counterparts. Unlike prior research, we did not find a clear magnitude cutoff or a strong association with fast meteor showers. Additionally, we note several interesting trends not previously reported, which include PT eligibility being primarily determined by a meteor's terminal height and an apparent dynamical origin dependence that likely reflects physical meteoroid properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".