The Frequency of Intrinsic X-Ray Weakness among Broad Absorption Line Quasars
Bibliographic record
Abstract
Abstract We present combined ≈14–37 ks Chandra observations of seven z = 1.6–2.7 broad absorption line (BAL) quasars selected from the Large Bright Quasar Survey (LBQS). These seven objects are high-ionization BAL (HiBAL) quasars, and they were undetected in the Chandra hard band (2–8 keV) in previous observations. The stacking analyses of previous Chandra observations suggested that these seven objects likely contain some candidates for intrinsically X-ray weak BAL quasars. With the new Chandra observations, six targets are detected. We calculate their effective power-law photon indices and hard-band flux weakness, and find that two objects, LBQS 1203+1530 and LBQS 1442–0011, show soft/steep spectral shapes ( and ) and significant X-ray weakness in the hard band (by factors of ≈15 and 12). We conclude that the two HiBAL quasars are good candidates for intrinsically X-ray weak BAL quasars. The mid-infrared-to-ultraviolet spectral energy distributions of the two candidates are consistent with those of typical quasars. We constrain the fraction of intrinsically X-ray weak active galactic nuclei (AGNs) among HiBAL quasars to be ≈7%–10% (2/29–3/29), and we estimate it is ≈6%–23% (2/35–8/35) among the general BAL quasar population. Such a fraction is considerably larger than that among non-BAL quasars, and we suggest that intrinsically X-ray weak quasars are preferentially observed as BAL quasars. Intrinsically X-ray weak AGNs likely comprise a small minority of the luminous type 1 AGN population, and they should not affect significantly the completeness of these AGNs found in deep X-ray surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".