A study of the stellar populations in the Kepler field
Bibliographic record
Abstract
Studying the formation and evolution of galaxies is a fundamental problem in astronomy that warrants repeated investigation by astronomers. How do spiral galaxies like our own come to have its distinct spiral arms? How do stars and their properties change over time and will they continue to evolve in the same manner? To try and attempt to answer such questions we need to look to our own Galaxy the Milky Way. Models have become an integral part of developing our understanding of the Galaxy but for these models to produce reliable data from which conclusions can be made upon, they must be shown to produce accurate and reasonable results. In this study a population synthesis code that produces synthetic stellar photometric data is compared to two surveys, 2MASS and Pan-STARRS, to analyse properties such as the total number counts and colour distributions. The parameters in the model were then changed in order to investigate how these effected the model's output. It was found from the different catalogues that the number counts from the model returned fewer stars, the amount of which varied with Galactic latitude with better agreement for the 2MASS survey. The colour of the model was then compared and found that for 2MASS the fit was much better than that of Pan-STARRS and that at higher Galactic latitudes the fit was slightly better. Investigating the input parameters to the model found that no change in the age metallicity relation and star formation rate in the thin disk gave any noticeable difference to either number counts or colour distribution, but for the case where no extinction was selected there were small changes in number counts for Pan-STARRS and expected changes in the colour distribution for both surveys. The initial mass function was also changed but it was found that the default Chabrier lognormal function was the best in simulating observed number counts.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".