Simultaneous Monitoring of X-Ray and Radio Variability in Sagittarius A*
Bibliographic record
Abstract
Abstract Monitoring of Sagittarius A* from X-ray to radio wavelengths has revealed structured variability—including X-ray flares—but it is challenging to establish correlations between them. Most studies have focused on variability in the X-ray and infrared, where variations are often simultaneous, and because long time series at submillimeter and radio wavelengths are limited. Previous work on submillimeter and radio variability hints at a lag between X-ray flares and their candidate submillimeter or radio counterparts, with the long wavelength data lagging the X-ray. However, there is only one published time lag between an X-ray flare and a possible radio counterpart. Here we report nine contemporaneous X-ray and radio observations of Sgr A*. We detect significant radio variability peaking minutes after the brightest X-ray flare ever detected from Sgr A*. We also report other potentially associated X-ray and radio variability, with the radio peaks appearing minutes after these weaker X-ray flares. Taken at face value, these results suggest that stronger X-ray flares lead to longer time lags in the radio. However, we also test the possibility that the variability at X-ray and radio wavelengths is not temporally correlated. We cross-correlate data from mismatched X-ray and radio epochs and obtain comparable correlations to the matched data. Hence, we find no overall statistical evidence that X-ray flares and radio variability are correlated, underscoring a need for more simultaneous, long duration X-ray–radio monitoring of Sgr A*.
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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 | 0.001 | 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 | 0.000 |
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".