Bibliographic record
Abstract
Abstract Measuring blackbody parameters for objects hotter than a few 10 4 K with optical data alone is common in many astrophysical studies. However this process is prone to large errors because at those temperatures the optical bands are mostly sampling the Rayleigh–Jeans tail of the spectrum. Here we quantify these errors by simulating different blackbodies, sampling them in various bands with realistic measurement errors, and refitting them to blackbodies using two different methods and two different priors. We find that when using only optical data, log-uniform priors perform better than uniform priors. Still, measured temperatures of blackbodies above ∼35,000 K can be wrong by ∼10,000 K, and only lower limits can be obtained for temperatures of blackbodies hotter than ∼50,000 K. Bolometric luminosities estimated from optical-only blackbody fits can be wrong by factors of 3–5. When adding space-based ultraviolet data, these errors shrink significantly. For when such data are not available, we provide plots and tables of the distributions of true temperatures that can result in various measured temperatures. It is important to take these distributions into account as systematic uncertainties when fitting hot blackbodies with optical data alone.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".