Ultraviolet Line Spectra of Metal‐poor Star‐forming Galaxies
Bibliographic record
Abstract
We present synthetic ultraviolet spectra of metal-poor star-forming galaxies that were calculated with the Starburst99 package. A new spectral library was generated from Hubble Space Telescope observations of O stars in the Large and Small Magellanic Clouds. The corresponding mean metallicity of the synthetic spectra is approximately Z ☉ . The spectra have a resolution of 1 Å and cover the spectral range 1200-1600 Å. A set of model spectra was calculated for a standard initial mass function and star formation history and is compared to synthetic spectra at solar metallicity. We find that the spectral lines are generally weaker at lower metallicity, as expected from the lower elemental abundances. Stellar wind lines, however, show a more complex behavior: the metallicity dependence of the ionization balance can be important in trace ions, like N 4+ and Si 3+ . Therefore, the strength of N V λ1240 and Si IV λ1400 does not scale monotonically with metallicity. We compare our new models to ultraviolet spectra of NGC 5253 and MS 1512-cB58, two star-forming galaxies with one-fourth solar metallicity at low and high redshifts, respectively. The new library provides significantly better fits to the observations than earlier models using the Z ☉ library. We discuss the potential of utilizing stellar photospheric and wind lines to estimate the chemical composition of star-forming galaxies. The new metal-poor synthetic spectra are available via the Starburst99 Web site.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".