The power of infrared AGN selection in mergers: a theoretical study
Bibliographic record
Abstract
The role of galaxy mergers in fuelling active galactic nuclei (AGNs) is still debated, owing partly to selection effects inherent to studies of the merger/AGN connection. In particular, luminous AGNs are often obscured in late-stage mergers. Mid-infrared (IR) colour selection of dust-enshrouded AGN with, e.g. the Wide-field Infrared Survey Explorer (WISE) has uncovered large new populations of obscured AGN. However, this method is sensitive mainly to AGN that dominate emission from the host. To understand how selection biases affect mid-IR studies of the merger/AGN connection, we simulate the evolution of AGN throughout galaxy mergers. Although mid-IR colours closely trace luminous, obscured AGN, we show that nearly half of merger-triggered AGNs are missed with common mid-IR selection criteria, even in late-stage, gas-rich major mergers. At z ≲ 0.5, where merger signatures and dual nuclei can most easily be detected, we find that a more lenient W1 − W2 > 0.5 cut greatly improves completeness without significantly decreasing reliability. Extreme nuclear starbursts are briefly able to mimic this AGN signature, but this is largely irrelevant in mergers, where such starbursts are accompanied by AGN. We propose a two-colour cut that yields high completeness and reliability even in starbursting systems. Further, we show that mid-IR colour selection very effectively identifies dual AGN hosts, with the highest fraction at the smallest separations (<3 kpc). Thus, many merger hosts of mid-IR AGN should contain unresolved dual AGN; these are ideal targets for high-resolution follow-up, particularly with the James Webb Space Telescope.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".