Optical Selection of Faint Active Galactic Nuclei in the COSMOS Field
Bibliographic record
Abstract
We outline a strategy to select faint ( i AB < 24.5) type 1 active galactic nucleus (AGN) candidates down to the Seyfert/QSO boundary for spectroscopic targeting in the COSMOS field. Our selection process picks candidates by their nonstellar colors in uBVRizK broadband photometry from the Subaru and CFH Telescopes and morphological properties extracted from Hubble Space Telescope ( HST ) ACS i -band data. Although the COSMOS field has been used extensively to survey the faint galaxy population out to z ∼ 6, AGN optical color selection has not been applied to so faint a level in such a large continuous part of the sky. Hot stars are known to be the dominant contaminant for bright AGN candidate selection at z < 2, but we anticipate the highest color contamination rate at all redshifts to be from faint starburst and compact galaxies. Morphological selection via the Gini Coefficient separates most potential AGNs from these faint blue galaxies. Recent models of the quasar luminosity function (QLF) from Hopkins et al. are used to estimate quasar surface densities, and a recent study of stellar populations in the COSMOS field is applied to infer stellar surface densities and contamination. We use 292 spectroscopically confirmed type 1 broad-line AGN and quasar templates to predict AGN colors as a function of redshift, and then contrast those predictions with the colors of known contaminating populations. Since the number of galaxy contaminants cannot be reliably identified with respect to stellar and predicted QLF numbers, the completeness and efficiency of the selection cannot be calculated before gathering confirming spectroscopic observations. Instead we offer an upper limit estimate to selection efficiency (about 50% for low- z and 20%-40% for intermediate- z and high- z ) as well as the completeness and efficiency with respect to an X-ray point source population (from the COSMOS AGN Survey), in the range 20%-50%. The motivation of this study and subsequent spectroscopic follow-up is to populate and refine the faint end of the QLF, at both low and high redshifts, where the population of type 1 AGNs is presently not well known. The anticipated AGN observations will add to the ~300 already known AGNs in the COSMOS field, making COSMOS a densely packed field of quasars to be used to understand supermassive black holes and probe the structure of the intergalactic medium in the intervening volume.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".