A versatile classification tool for galactic activity using optical and infrared colors
Bibliographic record
Abstract
Context.The overwhelming majority of diagnostic tools for galactic activity are focused mainly on the classes of active galaxies. Passive or dormant galaxies are often excluded from these diagnostics, which usually employ emission-line features (e.g., forbidden emission lines). Thus, most of them focus on specific types of activity or only on one activity class, for example active galactic nucleus (AGN) galaxies Aims.In this work we used infrared and optical colors to build an all-inclusive galactic activity diagnostic tool that can discriminate between star-forming, AGN, low-ionization nuclear emission-line region, composite, and passive galaxies, and which can be used in local and low-redshift galaxies. Methods.We used the random forest algorithm to define a new activity diagnostic tool. As the ground truth for the training of the algorithm, we considered galaxies that have been classified based on their optical spectral lines. We explored classification criteria based on infrared colors from the first three WISE bands (bands 1, 2, and 3) supplemented with optical colors from theu, g, andrSDSS bands. From them, we sought the combination with the minimum number of colors that provides optimal results. Furthermore, to mitigate biases related to aperture effects, we introduced a new WISE photometric scheme that combines apertures of different sizes. Results.Using machine learning methods, we developed a diagnostic tool that accommodates both active and passive galaxies under one unified classification scheme using just three colors. We find that the combination ofW1-W2, W2-W3, and g-r colors offers a good performance, while the broad availability of these colors for a large number of galaxies ensures it can be applied to large galaxy samples. The overall accuracy is ~81%, and the achieved completeness for each class is ~81% for star-forming, ~56% for AGN, ~68% for LINER, ~65% for composite, and ~85% for passive galaxies. Conclusions.Our diagnostic represents a significant improvement over existing infrared diagnostics because it includes all types of active galaxies, as well as passive galaxies, extending their application to the local Universe. The inclusion of the optical colors improves its ability to identify low-luminosity AGN galaxies, which are generally confused with star-forming galaxies, and helps us identify cases of starbursts with extreme mid-infrared colors that mimic obscured AGN galaxies, a well-known problem for most infrared diagnostics.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".