Relation between photometric and parameter errors of star clusters
Bibliographic record
Abstract
ABSTRACT Many observations supply only photometry data with large uncertainties. The study of star clusters based on such data is important for astrophysical studies, although photometric uncertainty affects the accuracy of results. In order to estimate the parameter uncertainties that are caused by photometric errors, this work quantifies the influence of photometric error on the uncertainties of some basic parameters when colour-magnitude diagram (CMD) fitting is used to determine these parameters. The observed data are derived from Hubble Space Telescope (HST) observations with the WFPC2 in four bands because of the large coverage of photometric error. The photometric errors that are estimated by Artificial Star Test (AST) are taken, and a binary star stellar population synthesis model and the Powerful CMD code are adopted to determine the parameters of star clusters. Two popular types of stellar population models, i.e. binary star simple stellar population (bsSSP) and single star simple stellar population (ssSSP), are used. The effects of photometric errors on the uncertainties of distance modulus (m − M), colour excess (E), metallicity (Z), and age (t), i.e. Δm − M, ΔE, ΔZ, and Δt, are studied via 19 globular clusters. The results show that Δm − M, ΔE, ΔZ, and Δt have positive correlations with photometric error. Some linear fitting formulae are given to make the results easy to use. The results can be used to estimate the errors of parameters that are caused by photometric error when determining the parameters by CMD fitting, for star clusters with relatively large photometric uncertainties (>∼0.01 mag).
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".