Atomic data for stellar astrophysics: from the UV to the IR<sup>1</sup>This article is part of a Special Issue on the 10th International Colloquium on Atomic Spectra and Oscillator Strengths for Astrophysical and Laboratory Plasmas.
Bibliographic record
Abstract
The study of stars and stellar evolution relies heavily on the analysis of stellar spectra. The need for atomic line data from the ultraviolet (UV) to the infrared (IR) regions is greater now than ever. In the past twenty years, the time since the launch of the Hubble Space Telescope, great progress has been made in acquiring atomic data for UV transitions. The optical wavelength region, now expanded by progress in detector technology, continues to provide motivation for new atomic data. In addition, investments in new instrumentation for ground-based and space observatories has lead to the availability of high-quality spectra at IR wavelengths, where the need for atomic data is most critical. In this review, examples are provided of the progress made in generating atomic data for stellar studies, with a look to the future for addressing the accuracy and completeness of atomic data for anticipated needs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".