ON R − W1 AS A DIAGNOSTIC TO DISCOVER OBSCURED ACTIVE GALACTIC NUCLEI IN WIDE-AREA X-RAY SURVEYS
Bibliographic record
Abstract
ABSTRACT Capitalizing on the all-sky coverage of WISE and the 35% and 50% sky coverage from Sloan Digital Sky Survey and Pan-STARRS, respectively, we explore the efficacy of m R (optical) – (mid-infrared), hereafter , as a color diagnostic to identify obscured supermassive black hole accretion in wide-area X-ray surveys. We use the ∼16.5 deg2 Stripe 82 X-ray survey data as a test bed to compare with R − K, an oft-used obscured active galactic nucleus (AGN) selection criterion, and examine where different classes of objects lie in this parameter space. Most stars follow a well-defined path in R − K versus space. We demonstrate that optically normal galaxies hosting X-ray AGNs at redshifts can be recovered with an color cut, while they typically are not selected as AGNs based on their colors. Additionally, different observed X-ray luminosity bins favor different regions in parameter space: moderate-luminosity AGNs (1043 erg erg s−1) tend to have red colors, while the highest-luminosity AGNs ( erg s−1) have bluer colors; higher spectroscopic completeness of the Stripe 82X sample is needed to determine whether this is a selection effect or an intrinsic property. Finally, we parameterize X-ray obscuration of Stripe 82X AGNs by calculating their hardness ratios (HRs) and find no clear trends between HR and optical reddening. Our results will help inform best-effort practices in following up obscured AGN candidates in current and future wide-area, shallow X-ray surveys, including the all-sky eROSITA mission.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".