MOMO. IV. The Complete Swift X-Ray and UV/Optical Light Curve and Characteristic Variability of the Blazar OJ 287 during the Last Two Decades
Bibliographic record
Abstract
Abstract We have been carrying out a dense monitoring of the blazar OJ 287 with Swift since late 2015 as part of our project MOMO (Multiwavelength Observations and Modeling of OJ 287). This is the densest existing monitoring of OJ 287 involving X-ray/UV data. In this latest publication of a sequence, we characterize the multiwavelength variability of OJ 287 based on >4000 Swift single-wave-band data sets including archival data since 2005. A structure function analysis reveals a characteristic timescale of ∼5 days in the optical–UV at epochs of low-level activity and larger during outbursts. The discrete correlation function shows zero lag between optical and UV, with τ = 0 ± 1 day at the epoch of densest cadence. During outbursts (in 2016/17 and 2020) the X-rays follow the UV with near-zero lags. However, during quiescence, the delay is 7–18 days with X-rays leading or lagging, interpreted as due to a different X-ray component dominated by inverse Compton emission. Scaling relations are used to derive the characteristic length scales of the broad-line region and torus in OJ 287. A remarkable, symmetric UV–optical deep fade is identified in late 2017, lasting 2 months. We rule out occultation from the passage of a dusty cloud and a model where the secondary black hole deflects the jet between the primary and observer. We speculate about a temporary dispersion or jet swing event in the core or in a bright quasi-stationary jet feature. The deep fade reveals an additional, spatially distinct X-ray component. The epoch 2020.9–2021.1 was searched for precursor flare activity predicted by the binary black hole model of OJ 287.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
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".