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 ∼10 4 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 deg 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".