Estimating the parameters of globular cluster M 30 (NGC 7099) from time-series photometry<i>(Corrigendum)</i>
Bibliographic record
Abstract
Aims. We present the analysis of 26 nights of V and I time-series observations from 2011 and 2012 of the globular cluster M 30 (NGC 7099). We \nused our data to search for variable stars in this cluster and refine the periods of known variables; we then used our variable star light curves to \nderive values for the cluster’s parameters. \nMethods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster \nparameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate was possible. We also \nderived an estimate for the age of the cluster by fitting theoretical isochrones to our colour-magnitude diagram (CMD). \nResults. Out of 13 stars previously catalogued as variables, we find that only 4 are bona fide variables. We detect two new RR Lyrae variables, \nand confirm two additional RR Lyrae candidates from the literature. We also detect four other new variables, including an eclipsing blue straggler \nsystem, and an SX Phoenicis star. This amounts to a total number of confirmed variable stars in M 30 of 12. We perform Fourier decomposition of \nthe light curves of the RR Lyrae stars to derive cluster parameters using empirical relations. We find a cluster metallicity [Fe/H]ZW = −2.01±0.04, \nor [Fe/H]UVES = −2.11 ± 0.06, and a distance of 8.32 ± 0.20 kpc (using RR0 variables), 8.10 kpc (using one RR1 variable), and 8.35 ± 0.42 kpc \n(using our SX Phoenicis star detection in M 30). Fitting isochrones to the CMD, we estimate an age of 13.0 ± 1.0 Gyr for M 30.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".