Heavily obscured AGN detection: A radio versus X-ray challenge
Bibliographic record
Abstract
Context. In the supermassive black hole (SMBH)-galaxy coevolution scenario, heavily obscured active galactic nuclei (AGN) represent a fundamental phase of SMBH growth during which most of the BH mass is accreted and the scaling relations with the host galaxy are set. Obscured nuclei are thought to constitute a major fraction of the whole AGN population, but their statistics and evolution across cosmic time are still highly uncertain. Therefore, it is pivotal to identify new ways to detect this vast and hidden population of growing SMBHs. A promising way to select heavily obscured AGN is through radio emission, which is largely unaffected by obscuration and can be used as a proxy for nuclear activity. Aims. In this work, we study the AGN radio detection effectiveness in the major deep extragalactic surveys, considering different AGN obscuration levels, redshift, and AGN bolometric luminosities. We particularly focus on comparing their radio and X-ray detectability, making predictions for present and future radio surveys. Methods. We extrapolated the predictions of the AGN population synthesis model of the cosmic X-ray background (CXB) to the radio band, by deriving the 1.4 GHz luminosity functions of unobscured (i.e., with hydrogen column densities log NH < 22), obscured (22 < log NH < 24), and Compton-thick (CTK, log NH > 24) AGN. We then used these functions to forecast the number of detectable AGN based on the area, flux limit, and completeness of a given radio survey and compare it with the AGN number resulting from X-ray predictions. Results. When applied to deep extragalactic fields covered both by radio and X-ray observations, we show that, while X-ray selection is generally more effective in detecting unobscured AGN, the surface density of CTK AGN radio detected is on average ten times larger than the X-ray one, and even greater at high redshifts, considering the current surveys and facilities. Our results suggest that thousands of CTK AGN are already present in current radio catalogs, but most of them escaped any detection in the corresponding X-ray observations. We also present expectations for the number of AGN to be detected by the Square Kilometer Array Observatory (SKAO) in its future deep and wide radio continuum surveys, finding that it will be able to detect more than 2000 AGN at z > 6 and tens of them at z > 10, more than half of which are expected to be CTK.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".