Project MOMO: Multiwavelength Observations and Modeling of OJ 287
Bibliographic record
Abstract
Our project MOMO (Multiwavelength observations and modeling of OJ 287) consists of dedicated, dense, long-term flux and spectroscopic monitoring, and deep follow-up observations of the blazar OJ 287 at >13 frequencies from the radio to the X-ray band since late 2015. In particular, we are using Swift to obtain optical-UV-X-ray spectral energy distributions (SEDs) and the Effelsberg telescope to obtain radio measurements between 2 and 40 GHz. MOMO is the densest long-term monitoring of OJ 287 involving X-rays and broad-band SEDs. The theoretical part of the project aims at understanding jet and accretion physics of the blazar central engine in general and the supermassive binary black hole scenario in particular. Results are presented in a sequence of publications and so far included: detection and detailed analysis of the bright 2016/17 and 2020 outbursts and the long-term light curve; Swift, XMM, and NuSTAR spectroscopy of the 2020 outburst around maximum; and interpretation of selected events in the context of the binary black hole scenario of OJ 287 (papers I–IV). Here, we provide a description of the project MOMO, a summary of previous results, the latest results, and we discuss future prospects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".