COMET 22P/KOPFF: DUST ENVIRONMENT AND GRAIN EJECTION ANISOTROPY FROM VISIBLE AND INFRARED OBSERVATIONS
Bibliographic record
Abstract
We present optical observations and Monte Carlo models of the dust coma, tail, and trail structures of the comet 22P/Kopff during the 2002 and 2009 apparitions. Dust loss rates, ejection velocities, and power-law size distribution functions are derived as functions of the heliocentric distance using pre- and post-perihelion imaging observations during both apparitions. The 2009 post-perihelion images can be accurately fitted by an isotropic ejection model. On the other hand, strong dust ejection anisotropies are required to fit the near-coma regions at large heliocentric distances (both inbound at r h = 2.5 AU and outbound at r h = 2.6 AU) for the 2002 apparition. These asymmetries are compatible with a scenario where dust ejection is mostly seasonally driven, coming mainly from regions near subsolar latitudes at far heliocentric distances inbound and outbound. At intermediate to near-perihelion heliocentric distances, the outgassing would affect much more extended latitude regions, the emission becoming almost isotropic near perihelion. We derived a maximum dust production rate of 260 kg s −1 at perihelion, and an averaged production rate over one orbit of 40 kg s −1 . An enhanced emission rate, also accompanied by a large ejection velocity, is predicted at r h > 2.5 pre-perihelion. The model has also been extended to the thermal infrared in order to be applied to available trail observations of this comet taken with IRAS and Infrared Space Observatory spacecrafts. The modeled trail intensities are in good agreement with those observations, which is remarkable taking into account that those data are sensitive to dust ejection patterns corresponding to several orbits before the 2002 and 2009 apparitions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".