A versatile classification tool for galactic activity using optical and infrared colors
Bibliographic record
Abstract
(abridged) The overwhelming majority of diagnostic tools for galactic activity are focused on active galaxies. Passive or dormant galaxies are often excluded from these diagnostics which usually employ emission line features. In this work, we use infrared and optical colors in order to build an all-inclusive galactic activity diagnostic tool that can discriminate between star-forming, AGN, LINER, composite, and passive galaxies, and which can be used in local and low-redshift galaxies. We explore classification criteria based on infrared colors from the 3 WISE bands supplemented with optical colors from the u, g, and r SDSS bands. From these we aim to find the minimal combination of colors for optimal results. Furthermore, to mitigate biases related to aperture effects, we introduce a new WISE photometric scheme combing different sized apertures. We develop a diagnostic tool using machine learning methods that includes both active and passive galaxies under one unified scheme using 3 colors. We find that the combination of W1-W2, W2-W3, and g-r colors offers good performance while the broad availability of these colors for a large number of galaxies ensures wide applicability on large galaxy samples. The overall accuracy is $\sim$81% while the achieved completeness for each class is $\sim$81% for star-forming, $\sim$56% for AGN, $\sim$68% for LINER, $\sim$65% for composite, and $\sim$85% for passive galaxies. Our diagnostic provides a significant improvement over existing IR diagnostics by including all types of active, as well as passive galaxies, and extending them to the local Universe. The inclusion of the optical colors improves their performance in identifying low-luminosity AGN which are generally confused with star-forming galaxies, and helps to identify cases of starbursts with extreme mid-IR colors which mimic obscured AGN galaxies, a well-known problem for most IR diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.005 | 0.004 |
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".