A targeted search for Main Belt Comets
Bibliographic record
Abstract
ABSTRACT Main Belt Comets (MBCs) exhibit sublimation-driven activity while occupying asteroid-like orbits in the Main Asteroid Belt. MBCs and candidates show stronger clustering of their longitudes of perihelion around 15° than other objects from the Outer Main Belt (OMB). This potential property of MBCs could facilitate the discovery of new candidates by observing objects in similar orbits. We acquired deep r-band images of 534 targeted asteroids using the Isaac Newton Telescope/Wide Field Camera between 2018 and 2020. Our sample is comprised of OMB objects observed near perihelion, with longitudes of perihelion between 0 and 30° and orbital parameters similar to known MBCs. Our pipeline applied activity detection methods to 319 of these objects to look for tails or comae, and we visually inspected the remaining asteroids. Our activity detection pipeline highlighted a faint antisolar tail-like feature around 2001 NL19 (279870) observed on 2018 November 07, 6 months after perihelion. This is consistent with cometary activity; however, additional observations of this object will be needed during its next perihelion to investigate its potential MBC status. If it is active our survey yields a detection rate of ∼1:300, which is higher than previous similar surveys, supporting the idea of dynamical clustering of MBCs. If not, it is consistent with previously estimated abundance rates of MBCs in the OMB (<1:500).
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".