SYMBA : an end-to-end VLBI synthetic data generation pipeline simulating event horizon telescope observations of M 87
Bibliographic record
Abstract
CONTEXT: Realistic synthetic observations of theoretical source models are essential for our understanding of real observational data. In using \nsynthetic data, one can verify the extent to which source parameters can be recovered and evaluate how various data corruption effects can be \ncalibrated. These studies are the most important when proposing observations of new sources, in the characterization of the capabilities of new or \nupgraded instruments, and when verifying model-based theoretical predictions in a direct comparison with observational data. \nAIMS: We present the SYnthetic Measurement creator for long Baseline Arrays (SYMBA), a novel synthetic data generation pipeline for Very Long \nBaseline Interferometry (VLBI) observations. SYMBA takes into account several realistic atmospheric, instrumental, and calibration effects. \nMETHODS: We used SYMBA to create synthetic observations for the Event Horizon Telescope (EHT), a millimetre VLBI array, which has recently \ncaptured the first image of a black hole shadow. After testing SYMBA with simple source and corruption models, we study the importance of \nincluding all corruption and calibration effects, compared to the addition of thermal noise only. Using synthetic data based on two example general \nrelativistic magnetohydrodynamics (GRMHD) model images of M 87, we performed case studies to assess the image quality that can be obtained \nwith the current and future EHT array for different weather conditions. \nRESULTS: Our synthetic observations show that the effects of atmospheric and instrumental corruptions on the measured visibilities are significant. \nDespite these effects, we demonstrate how the overall structure of our GRMHD source models can be recovered robustly with the EHT2017 array \nafter performing calibration steps, which include fringe fitting, a priori amplitude and network calibration, and self-calibration. With the planned \naddition of new stations to the EHT array in the coming years, images could be reconstructed with higher angular resolution and dynamic range. \nIn our case study, these improvements allowed for a distinction between a thermal and a non-thermal GRMHD model based on salient features in \nreconstructed images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".