FRIPON: a worldwide network to track incoming meteoroids
Bibliographic record
Abstract
Context. Near-Earth objects (NEOs) are the most accessible small Solar System bodies by both spacecrafts and ground-based telescopes. Close encounters of these objects with Earth represent opportunities to characterize their physical and mineralogical properties. They are also a constant threat to humanity due to possible impact events with Earth. In this context, the NEOROCKS project has been financed by the European Union’s Horizon 2020 research and innovation program. Aims. We present the final results on photometry of the NEOROCKS project, with the aim of extending the dataset of surface colors for small NEOs with unknown properties and, when possible, characterizing newly discovered NEOs. Methods. Photometric observations were performed using the 1.2 m telescope at the Haute-Provence observatory (in France) in the BVRI filters of the Johnson-Cousins photometric systems between May 2022 and June 2023. The stability and dynamics of objects from the NEOROCKS database was investigated by numerical integration. Results. We obtained new surface colors for 83 NEOs. Overall, the NEOROCKS color database contains 170 objects. The majority of the objects in the dataset with diameters D<500 m belong to a group of silicate bodies. We estimated the unbalanced percentage between S- and C-type objects as an observational bias due to reflective proprieties of the surface of objects. The average of Lyapunov time of about 100 years is evidence of highly chaotic orbits of objects from the color database of NEOROCKS. Asteroid 2011 OL51 has a reasonable probability of being a parent body contributor to the October Capricornidis meteor shower. Asteroids 2004 HK33, 2022 VV (D-type), 2003 WR21, and 2017 SE1 (A-type) belong to end-member classes and have ΔV<7 km/s; thus, they are possible candidates for in situ investigations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".