Chroma+GAS: An Expedited Solution for the Chemical Equilibrium for Cool Stellar Atmospheres
Bibliographic record
Abstract
We describe a unique approach to economizing the solution to the general chemical equilibrium and equation-of-state problem for late-type stars, including diatomic and polyatomic molecules, that is fast, accurate, and suitable for responsive approximate data modeling applications, and to more intensive modeling approaches in which the calculation of the gas equilibrium must be expedited to allow other aspects to be treated more realistically. The method, based on a novel economization of Newton's method of solution of the linearized Saha and conservation equations, has been implemented in Python and made available as a stand-alone package, GASPy, and has been integrated into the interactive Python atmosphere and spectrum modeling code ChromaStarPy. As a result, ChromaStarPy now computes the state of the gas, the number density of absorbers, and the surface flux spectrum, with consistent inclusion of 105 chemical species, including 34 diatomic, and 16 polyatomic, neutral molecules, as well as H − and , as well as many neutral and ionized atomic species. The economized method converges very rapidly and greatly improves the code's relevance to late-type stellar and brown dwarf spectrum modeling. We provide a brief overview of the GAS methodology, and present some illustrative results for the chemical equilibrium and spectrum for an M-type bright giant and dwarf, and a comparison to results of the PHOENIX/PPRESS package. All codes are available from the OpenStars www site: www.ap.smu.ca/OpenStars .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".