Astrometric Jitter as a Detection Diagnostic for Recoiling and Slingshot Supermassive Black Hole Candidates
Bibliographic record
Abstract
Abstract Supermassive black holes (SMBHs) can be ejected from their galactic centers due to gravitational wave recoil or the slingshot mechanism following a galaxy merger. If an ejected SMBH retains its inner accretion disk, it may be visible as an off-nuclear active galactic nucleus (AGN). At present, only a handful of offset AGNs that are recoil or slingshot candidates have been found, and none have been robustly confirmed. Compiling a large sample of runaway SMBHs would enable us to constrain the mass and spin evolution of binary SMBHs and study feedback effects of displaced AGNs. We adapt the method of varstrometry—which was developed for Gaia observations to identify off-center, dual, and lensed AGNs—in order to quickly identify off-nuclear AGNs in optical survey data by looking for an excess of blue versus red astrometric jitter. We apply this to the Pan-STARRS1 3π Survey and report on five new runaway AGN candidates. We focus on ZTF18aajyzfv: a luminous quasar offset by 6.7 ± 0.2 kpc from an adjacent galaxy at z = 0.224, and conclude after Keck LRIS spectroscopy and comparison to ASTRID simulation analogs that it is likely a dual AGN. This selection method can be easily adapted to work with data from the soon-to-be commissioned Vera C. Rubin Telescope Legacy Survey of Space and Time (LSST). LSST will have a higher cadence and deeper magnitude limit than Pan-STARRS1, and should permit detection of many more runaway SMBH candidates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".