WHaD diagram: Classifying the ionizing source with one single emission line
Bibliographic record
Abstract
Context. The usual approach to classify the ionizing source using optical spectroscopy is based on the use of diagnostic diagrams that compare the relative strength of pairs of collisitional metallic lines (e.g., [O III ] and [N II ]) to recombination hydrogen lines (e.g., H β and H α ). Despite it having been accepted as the standard procedure, it presents known problems, including confusion regimes and/or limitations related to the required signal-to-noise (S/N) of the emission lines involved. These problems not only affect our intrinsic understanding of the interstellar medium and its properties, but also the fundamental galaxy properties, such as the star formation rate and the oxygen abundance. This raises key questions related to the fraction of active galactic nuclei and other essential parameters. Aims. We attempt to minimize the problems introduced by the use of these diagrams, in particular, their implementation when the available information is limited due to either the fact that not all lines are available or they do not have the required S/N value. Methods. We explored the existing alternatives in the literature to minimize the confusion among different ionizing sources. We have proposed a new, simple diagram that uses the equivalent width and the velocity dispersion from one single emission line, H α , to classify the ionizing sources. Results. We used aperture-limited and spatially resolved spectroscopic data from the nearby Universe ( z ∼ 0.01) to demonstrate that the new diagram, which we have named WHaD, segregates the different ionizing sources in a more efficient way than earlier procedures. A new set of regions have been defined in this diagram to select among different ionizing sources. Conclusions. The new proposed diagram is well positioned to assist in determining the ionizing source when only H α is available or when the S/N of the emission lines is too low to obtain reliable fluxes for the weakest emission lines in classical diagnostic diagrams (e.g., H β ).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 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".