Quasars and the Big Blue Bump
Bibliographic record
Abstract
We investigate the ultraviolet-to-optical spectral energy distributions of 17 active galactic nuclei (AGNs) using quasi-simultaneous spectrophotometry spanning 900-9000 Å (rest frame). We employ data from the Far Ultraviolet Spectroscopic Explorer , the Hubble Space Telescope , and the 2.1 m telescope at Kitt Peak National Observatory. Taking advantage of the short-wavelength coverage, we are able to study the so-called big blue bump, the region in which the energy output peaks, in detail. Most objects exhibit a spectral break around 1100 Å. Although this result is formally associated with large uncertainty for some objects, there is strong evidence in the data that the far-ultraviolet spectral region is below the extrapolation of the near-ultraviolet-optical slope, indicating a spectral break around 1100 Å. We compare the behavior of our sample to those of non-LTE thin-disk models covering a range in black hole mass, Eddington ratio, disk inclination, and other parameters. The distribution of ultraviolet-optical spectral indices redward of the break and far-ultraviolet indices shortward of the break are in rough agreement with the models. However, we do not see a correlation between the far-ultraviolet spectral index and the black hole mass, as seen in some accretion disk models. We argue that the observed spectral break is intrinsic to AGNs, although intrinsic reddening as well as Comptonization can strongly affect the far-ultraviolet spectral index. We make our data available online in digital format.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".