The Open Cluster Chemical Abundances and Mapping Survey. VII. APOGEE DR17 [C/N]–Age Calibration
Bibliographic record
Abstract
Abstract Large-scale surveys open the possibility to investigate Galactic evolution both chemically and kinematically; however, reliable stellar ages remain a major challenge. Detailed chemical information provided by high-resolution spectroscopic surveys of the stars in clusters can be used as a means to calibrate recently developed chemical tools for age-dating field stars. Using data from the Open Cluster Abundances and Mapping survey, based on the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment 2 survey, we derive a new empirical relationship between open cluster stellar ages and the carbon-to-nitrogen ([C/N]) abundance ratios for evolved stars, primarily those on the red giant branch. With this calibration, [C/N] can be used as a chemical clock for evolved field stars to investigate the formation and evolution of different parts of our Galaxy. We explore how mixing effects at different stellar evolutionary phases, like the red clump, affect the derived calibration. We have established the [C/N]–age calibration for APOGEE Data Release 17 (DR17) giant star abundances to be <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:msub> <mml:mrow> <mml:mo stretchy="false">[</mml:mo> <mml:mi>Age</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>yr</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">]</mml:mo> </mml:mrow> <mml:mrow> <mml:mi>DR</mml:mi> <mml:mn>17</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>10.14</mml:mn> <mml:mspace width="0.25em"/> <mml:mo stretchy="false">(</mml:mo> <mml:mo>±</mml:mo> <mml:mn>0.08</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mo>+</mml:mo> <mml:mn>2.23</mml:mn> <mml:mo stretchy="false">(</mml:mo> <mml:mo>±</mml:mo> <mml:mn>0.19</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mspace width="0.25em"/> <mml:mo stretchy="false">[</mml:mo> <mml:mi mathvariant="normal">C</mml:mi> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi mathvariant="normal">N</mml:mi> <mml:mo stretchy="false">]</mml:mo> </mml:math> , usable for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>8.62</mml:mn> <mml:mo>≤</mml:mo> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>Age</mml:mi> <mml:mo stretchy="false">[</mml:mo> <mml:mi>yr</mml:mi> <mml:mo stretchy="false">]</mml:mo> <mml:mo stretchy="false">)</mml:mo> <mml:mo>≤</mml:mo> <mml:mn>9.82</mml:mn> </mml:math> , derived from a uniform sample of 49 clusters observed as part of APOGEE DR17 applicable primarily to metal-rich, thin- and thick-disk giant stars. This measured [C/N]–age APOGEE DR17 calibration is also shown to be consistent with asteroseismic ages derived from Kepler photometry.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".