Synthetic High‐Resolution Line Spectra of Star‐forming Galaxies below 1200 A
Bibliographic record
Abstract
We have generated a set of far-ultraviolet stellar libraries using spectra of OB and Wolf-Rayet stars in the Galaxy and the Large and Small Magellanic Cloud. The spectra were collected with the Far Ultraviolet Spectroscopic Explorer and cover a wavelength range from 1003.1 to 1182.7 Å at a resolution of 0.127 Å. The libraries extend from the earliest O to late-O and early-B stars for the Magellanic Cloud and Galactic libraries, respectively. Attention is paid to the complex blending of stellar and interstellar lines, which can be significant, especially in models using Galactic stars. The most severe contamination is due to molecular hydrogen. Using a simple model for the H 2 line strength, we were able to remove the molecular hydrogen lines in a subset of Magellanic Cloud stars. Variations of the photospheric and wind features of C III λ1176, O VI λλ1032, 1038, P V λλ1118, 1128, and S IV λλ1063, 1073, 1074 are discussed as a function of temperature and luminosity class. The spectral libraries were implemented into the LavalSB and Starburst99 packages and used to compute a standard set of synthetic spectra of star-forming galaxies. Representative spectra are presented for various initial mass functions and star formation histories. The valid parameter space is confined to the youngest ages of less than ≃10 Myr for an instantaneous burst, prior to the age when incompleteness of spectral types in the libraries sets in. For a continuous burst at solar metallicity, the parameter space is not limited. The suite of models is useful for interpreting the rest-frame far-ultraviolet in local and high-redshift galaxies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".