NON-LOCAL THERMODYNAMIC EQUILIBRIUM 1.5D MODELING OF RED GIANT STARS
Bibliographic record
Abstract
Spectra for two-dimensional (2D) stars in the 1.5D approximation are created from synthetic spectra of one-dimensional (1D) non-local thermodynamic equilibrium (NLTE) spherical model atmospheres produced by the PHOENIX code. The 1.5D stars have the spatially averaged Rayleigh–Jeans flux of a K3–4 III star while varying the temperature difference between the two 1D component models (Δ T 1.5D ) and the relative surface area covered. Synthetic observable quantities from the 1.5D stars are fitted with quantities from NLTE and local thermodynamic equilibrium (LTE) 1D models to assess the errors in inferred T eff values from assuming horizontal homogeneity and LTE. Five different quantities are fit to determine the T eff of the 1.5D stars: UBVRI photometric colors, absolute surface flux spectral energy distributions (SEDs), relative SEDs, continuum normalized spectra, and TiO band profiles. In all cases except the TiO band profiles, the inferred T eff value increases with increasing Δ T 1.5D . In all cases, the inferred T eff value from fitting 1D LTE quantities is higher than from fitting 1D NLTE quantities and is approximately constant as a function of Δ T 1.5D within each case. The difference between LTE and NLTE for the TiO bands is caused indirectly by the NLTE temperature structure of the upper atmosphere, as the bands are computed in LTE. We conclude that the difference between T eff values derived from NLTE and LTE modeling is relatively insensitive to the degree of the horizontal inhomogeneity of the star being modeled and largely depends on the observable quantity being fit.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".