THE EXTREME ULTRAVIOLET VARIABILITY OF QUASARS
Bibliographic record
Abstract
ABSTRACT We study the extreme ultraviolet (EUV) variability (rest frame wavelengths 500–920 Å) of high-luminosity quasars using Hubble Space Telescope (HST) (low to intermediate redshift sample) and Sloan Digital sky Survey (SDSS) (high redshift sample) archives. The combined HST and SDSS data indicates a much more pronounced variability when the sampling time between observations in the quasar rest frame is compared to s. Based on an excess variance analysis, for time intervals in the quasar rest frame, 10% of the quasars (4/40) show evidence of EUV variability. Similarly, for time intervals in the quasar rest frame, 55% of the quasars (21/38) show evidence of EUV variability. The propensity for variability does not show any statistically significant change between and (1 year). The temporal behavior is one of a threshold time interval for significant variability as opposed to a gradual increase on these timescales. A threshold timescale can indicate a characteristic spatial dimension of the EUV region. We explore this concept in the context of the slim disk models of accretion. We find that for rapidly spinning black holes, the radial infall time to the plunge region of the optically thin surface layer of the slim disk that is responsible for the preponderance of the EUV flux emission (primarily within 0–7 black hole radii from the inner edge of the disk) is consistent with the empirically determined variability timescale.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".