The Search for Binary Supermassive Black Holes among Quasars with Offset Broad Lines Using the Very Long Baseline Array
Bibliographic record
Abstract
Abstract In several previous studies, quasars exhibiting broad emission lines with ≳1000 km s −1 velocity offsets with respect to the host galaxy rest frame have been discovered. One leading hypothesis for the origin of these velocity-offset broad lines is the dynamics of a binary supermassive black hole (SMBH). We present high-resolution radio imaging of 34 quasars showing these velocity-offset broad lines with the Very Long Baseline Array (VLBA), aiming to find evidence for the putative binary SMBHs (such as dual radio cores), and testing the competing physical models. We detect exactly half of the target sample from our VLBA imaging, after implementing a 5 σ detection limit. While we do not resolve double radio sources in any of the targets, we obtain limits on the instantaneous projected separations of a radio-emitting binary for all of the detected sources under the assumption that a binary still exists within our VLBA angular resolution limits. We also assess the likelihood that a radio-emitting companion SMBH exists outside of our angular resolution limits, but its radio luminosity is too weak to produce a detectable signal in the VLBA data. Additionally, we compare the precise sky positions afforded by these data to optical positions from both the SDSS and Gaia DR2 source catalogs. We find projected radio/optical separations on the order of ∼10 pc for three quasars. Finally, we explore how future multi-wavelength campaigns with optical, radio, and X-ray observatories can help discriminate further between the competing physical models.
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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 | 0.001 | 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".