Taxonomic Classification of Asteroids via Broadband Near-Infrared Photometry
Bibliographic record
Abstract
For faint asteroids, it is not practical to obtain near-infrared spectra. However, it may be possible to use broadband photometry to infer spectral classifications and study composition. As a test of this, we processed SpeX near-infrared asteroid spectral data to simulate colors that would be obtained through photometry. We have found that certain color combinations (for example, z-J and H-K) can prove diagnostic in asteroid spectral classification. To this end, we have defined certain color-color regions that make it possible to define an asteroid as being a likely candidate for a certain spectral type. The regions identified define V and D type asteroids, the S-Q group, and the C-X group. Knowledge and use of these regions will significantly increase the usefulness of NIR broadband photometry in the study of near earth objects and allow characterization of asteroids that are too faint to be observed spectroscopically. Work on this project is made possible through the NSF REU program at Northern Arizona University and by funding from the Spitzer Space Telescope/JPL/Caltech.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".