Radio Variability from a Quiescent Stellar-mass Black Hole Jet
Bibliographic record
Abstract
Abstract Relativistic outflows are believed to be a common feature of black hole X-ray binaries (BHXBs) at the lowest accretion rates, when they are in their “quiescent” spectral state. However, we still lack a detailed understanding of how quiescent jet emission varies with time. Here we present 24 yr of archival radio observations (from the Very Large Array and the Very Long Baseline Array) of the BHXB V404 Cygni in quiescence (totaling 150 observations from 1.4 to 22 GHz). The observed flux densities follow lognormal distributions with means and standard deviations of and at 4.9 and 8.4 GHz, respectively (where f ν is the flux density in units of mJy). As expected, the average radio spectrum is flat with a mean and standard deviation of , where . We find that radio flares that increase the flux density by factors of 2–4 over timescales as short as <10 minutes are commonplace, and that long-term variations (over 10–4000 day timescales) are consistent with shot-noise impulses that decay to stochastic variations on timescales ≲10 days (and perhaps as short as tens of minutes to several hr). We briefly compare the variability characteristics of V404 Cygni to jetted active galactic nuclei, and we conclude with recommendations on how to account for variability when placing quiescent BHXB candidates with radio luminosities comparable to V404 Cygni ( L R ≈ 10 28 erg s −1 ) onto the radio/X-ray luminosity plane.
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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 | 0.000 |
| 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".