The WHaD diagram: Classifying the ionizing source with one single emission line
Bibliographic record
Abstract
The usual approach to classify the ionizing source using optical spectroscopy is based on the use of diagnostic diagrams that compares the relative strength of pairs of collisitional metallic lines (e.g., [O iii] and [N ii]) with respect to recombination hydrogen lines (e.g., Hβ and Hα). Despite of being accepted as the standard procedure, it present known problems, including confusion regimes and/or limitations related to the required signal-to-noise of the involved emission lines. These problems affect not only our intrinsic understanding of inter-stellar medium and its poroperties, but also fundamental galaxy properties, such as the star-formation rate and the oxygen abundance, and key questions just as the fraction of active galactic nuclei, among several others. We explore the existing alternatives in the literature to minimize the confusion among different ionizing sources and proposed a new simple diagram that uses the equivalent width and the velocity dispersion from one single emission line, Hα, to classify the ionizing sources. We use aperture limited and spatial resolved spectroscopic data in the nearby Universe (z{\sim}0.01) to demonstrate that the new diagram, that we called WHaD, segregates the different ionizing sources in a more efficient way that previously adopted procedures. A new set of regions are defined in this diagram to select betweeen different ionizing sources. The new proposed diagram is well placed to determine the ionizing source when only Hα is available, or when the signal-to-noise of the emission lines involved in the classical diagnostic diagrams (e.g., Hβ).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".