A catalogue of candidate milliparsec-separation massive black hole binaries from long-term optical photometric monitoring
Bibliographic record
Abstract
Context. The role of mergers in the evolution of massive black holes is still unclear, and their dynamical evolution from the formation of pairs to binaries and the final coalescence carries large physical uncertainties. The identification of the elusive population of close massive binary black holes (MBBHs) is crucial to understand the importance of mergers in the formation and evolution of SMBHs. Aims. It has been proposed that MBBHs may display periodic optical or ultraviolet variability. Optical surveys provide photometric measurements of a large variety of objects over decades, and searching for periodicities coming from galaxies in their long-term optical or UV light curves may help identify new MBBH candidates. Methods. Using the Catalina Real-Time Transient Survey (CRTS) and Zwicky Transient Facility (ZTF) data, we studied the long-term periodicity of variable sources in the centre of galaxies identified using the galaxy catalogue Glade+. Results. We report 36 MBBH candidates, with sinusoidal variability with amplitudes between 0.1 and 0.8 magnitudes over 3−5 cycles, through fitting 15 years of data. The periodicities are also detected when adding a red noise contribution to the sine model. Moreover, the periodicities are corroborated through generalized Lomb-Scargle (GLS) periodogram analysis, providing supplementary evidence for the observed modulation. We also indicate 58 objects that were previously proposed to be MBBH candidates from analysis of CRTS data only. Adding ZTF data clearly shows that the previously claimed modulation is due to red noise. We also created a catalogue of 221 weaker candidates which require further observations over the coming years to help validate their nature. Based on our 36 MBBH candidates, we expect ∼20 MBBHs at z<1, which is commensurate with simulations. Further observations will help confirm these results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".