Beyond diagnostic-diagrams: A critical exploration of the classification of ionization processes
Bibliographic record
Abstract
Context. Diagnostic diagrams based on optical emission lines, especially classical BPT diagrams, have long been used to distinguish the dominant ionisation mechanisms in galaxies. However, these methods suffer from degeneracies and limitations, particularly when applied to complex systems such as galaxies, where multiple ionisation sources coexist. Aims. We aim to critically assess the effectiveness of commonly used diagnostic diagrams in identifying star-forming galaxies, retired galaxies (RGs), and active galactic nuclei (AGNs). We also explore alternative diagnostics and propose a revised classification scheme to reduce misclassifications and better reflect the physical mechanisms ionizing gas in galaxies. Methods. Using a comprehensive sample of nearby galaxies from the NASA-Sloan Atlas (NSA) cross-matched with Sloan Digital Sky Survey (SDSS) spectroscopic data, we defined archetypal subsamples of late-type and star-forming galaxies, early-type and retired galaxies, and multiwavelength-selected AGNs. We evaluated their distribution across classical and more recent diagnostic diagrams, including the WHaN, WHaD, and a newly proposed WHaO diagram, which combine Hα equivalent width with additional indicators (N II/Hα, σHα and O III/O II, respectively). We carried out a quantitative comparison of the resulting classification across multiple schemes. Results. Classical BPT diagrams systematically overestimate the number of star-forming galaxies (∼10%) and misclassify a significant fraction of AGNs (up to 45%) and RGs (up to 100%). Diagrams incorporating the equivalent width of Hα, such as WHaN, WHaD, or WHaO, yield more reliable separations (with ∼20% of AGNs and ∼15% of RGs erroneously classified). A new classification scheme based on EW(Hα) thresholds and concordant WHaD/WHaO results achieves an improved level of purity for all classes (with ∼8–25% sources erroneously classified) and a better alignment with known physical properties. Conclusions. The widely used BPT-based classifications fail to accurately distinguish between ionisation mechanisms, especially in galaxies hosting low-luminosity AGNs or retired stellar populations. Updated schemes incorporating EW(Hα) and complementary diagnostics, despite their respective limitations, provide a more accurate view of galaxy ionisation and should be adopted in future studies of galaxy populations and evolution.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".