NLTE AND LTE Lick indices for red giants from [Fe/H] 0.0 TO −6.0 AT SDSS AND IDS spectral resolution
Bibliographic record
Abstract
We investigate the dependence of the complete system of 22 Lick indices on overall metallicity scaled from solar abundances, , from the solar value, 0.0, down to the extremely metal-poor (XMP) value of −6.0, for late-type giant stars (MK luminosity class III, ) of MK spectral class late-K to late-F ( K) of the type that are detected as "fossils" of early galaxy formation in the Galactic halo and in extra-galactic structures. Our investigation is based on synthetic index values, I, derived from atmospheric models and synthetic spectra computed with PHOENIX in Local Thermodynamic Equilibrium (LTE) and Non-LTE (NLTE), where the synthetic spectra have been convolved to the spectral resolution, R, of both IDS and SDSS (and LAMOST) spectroscopy. We identify nine indices, that we designate "Lick-XMP," that remain both detectable and significantly -dependent down to values of at least , and down to in five cases, while also remaining well-behaved (single-valued as a function of and positive in linear units). For these nine indices, we study the dependence of I on NLTE effects, and on spectral resolution. For our LTE I values for spectra of SDSS resolution, we present the fitted polynomial coefficients, , from multi-variate linear regression for I with terms up to third order in the independent variable pairs (, ) and (, ), and compare them to the fitted values of Worthey et al. at IDS spectral resolution. For this fitted I data-set we present tables of LTE partial derivatives, , , , and , that can be used to infer the relation between a given difference, , and a difference or , or a difference , while the other parameters are held fixed. For Fe-dominated Lick indices, the effect of NLTE is to generally weaken the value of I at any given and values. As an example of the impact on stellar parameter estimation, for late-type giants of inferred K, an Fe-dominated I value computed in LTE that is too strong might be compensated for by inferring a value that is too large.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".