THE EXTREME ULTRAVIOLET DEFICIT: JET CONNECTION IN THE QUASAR 1442+101
Bibliographic record
Abstract
In previous studies, it has been shown that the long-term time-averaged jet power, is correlated with spectral index in the extreme ultraviolet (EUV), α EUV (defined by computed between 700 and 1100 Å ). Larger tends to decrease the EUV emission. This is a curious relationship because it connects a long-term average over ∼10 6 years with an instantaneous measurement of the EUV. The EUV appears to emit adjacent to the central supermassive black hole and the most straightforward explanation of this correlation is that the EUV-emitting region interacts in real time with the jet-launching mechanism. Alternatively stated, the – α EUV correlation is a manifestation of a contemporaneous (real time) jet power, Q ( t ), correlation with α EUV . In order to explore this possibility, this paper considers the time variability of the strong radio jet of quasar 1442+101, which is not aberrated by strong Doppler enhancement. This high-redshift ( z = 3.55) quasar is uniquely suited for this endeavor as the EUV is redshifted into the optical observing window allowing for convenient monitoring. More importantly, it is bright enough to be seen through the Lyman forest and its radio flux is strong enough that it has been monitored frequently. Quasi-simultaneous monitoring (five epochs spanning ∼40 years) shows that increases in Q ( t ) correspond to decreases in the EUV as expected.
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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 | 0.000 |
| 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".