The Flux Distribution and Sky Density of 25th Magnitude Main Belt Asteroids
Bibliographic record
Abstract
Abstract Digital tracking enables telescopes to detect asteroids several times fainter than conventional techniques. We describe our optimized methodology to acquire, process, and interpret digital tracking observations, and we apply it to probe the apparent magnitude distribution of main belt asteroids fainter than any previously detected from the ground. All-night integrations with the Dark Energy Camera (DECam) yield 95% completeness at R magnitude 25.0 and useful sensitivity to R = 25.6 mag when we use an analytical detection model to correct flux overestimation bias. In a single DECam field observed over two nights, we detect a total of 3234 distinct asteroids, of which 3123 are confirmed on both nights. At opposition from the Sun, we find a sky density of 697 ± 15 asteroids per square degree brighter than R = 25.0 mag, and 1031 ± 23 brighter than R = 25.6 mag. We agree with published results for the sky density and apparent magnitude distribution of asteroids brighter than R = 23 mag. For a power law defined by dN / dR ∝ 10 αR , we find marginally acceptable fits with a constant slope α =0.28 ± 0.02 from R = 20 to 25.6 mag. Better fits are obtained for a broken power law with α = 0.218 ± 0.026 for R = 20–23.5 mag, steepening to α = 0.340 ± 0.025 for R = 23.5–25.6 mag. The constant or steepening power law indicates that asteroids fainter than R = 23.5 mag are abundant, contrary to some previous claims but consistent with theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".