A Large, Uniform Sample of X-Ray-emitting Active Galactic Nuclei from the ROSAT All Sky and Sloan Digital Sky Surveys: The Data Release 5 Sample
Bibliographic record
Abstract
We describe further results of a program aimed at yielding ∼104 fully characterized optical identifications of ROSAT X-ray sources. Our program employs X-ray data from the ROSAT All Sky Survey (RASS) and both optical imaging and spectroscopic data from the Sloan Digital Sky Survey (SDSS). RASS/SDSS data from 5740 deg2 of sky spectroscopically covered in SDSS Data Release 5 provide an expanded catalog of 7000 confirmed quasars and other active galactic nuclei (AGNs) that are probable RASS identifications. Again, in our expanded catalog the identifications as X-ray sources are statistically secure, with only a few percent of the SDSS AGNs likely to be randomly superposed on unrelated RASS X-ray sources. Most identifications continue to be quasars and Seyfert 1 galaxies with 15 < m < 21 and 0.01 < z < 4, but the total sample size has grown to include very substantial numbers of even quite rare AGNs, e.g., it now includes several hundreds of candidate X-ray-emitting BL Lac objects and narrow-line Seyfert 1 galaxies. In addition to exploring rare subpopulations, such a large total sample may be useful when considering correlations between the X-ray and the optical and may also serve as a resource list from which to select the "best" object (e.g., X-ray-brightest AGN of a certain subclass at a preferred redshift or luminosity) for follow-up X-ray spectral or alternate detailed studies.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".