IMAGING THE SUPERMASSIVE BLACK HOLE SHADOW AND JET BASE OF M87 WITH THE EVENT HORIZON TELESCOPE
Bibliographic record
Abstract
The Event Horizon Telescope (EHT) is a project to assemble a Very Long Baseline Interferometry (VLBI) network of millimeter wavelength dishes that can resolve strong field general relativistic signatures near a supermassive black hole. As planned, the EHT will include enough dishes to enable imaging of the predicted black hole "shadow," a feature caused by severe light bending at the black hole boundary. The center of M87, a giant elliptical galaxy, presents one of the most interesting EHT targets as it exhibits a relativistic jet, offering the additional possibility of studying jet genesis on Schwarzschild radius scales. Fully relativistic models of the M87 jet that fit all existing observational constraints now allow horizon-scale images to be generated. We perform realistic VLBI simulations of M87 model images to examine the detectability of the black shadow with the EHT, focusing on a sequence of model images with a changing jet mass load radius. When the jet is launched close to the black hole, the shadow is clearly visible both at 230 and 345 GHz. The EHT array with a resolution of 20–30 μas resolution (∼2–4 Schwarzschild radii) is able to image this feature independent of any theoretical models and we show that imaging methods used to process data from optical interferometers are applicable and effective for EHT data sets. We demonstrate that the EHT is also capable of tracing real-time structural changes on a few Schwarzschild radii scales, such as those implicated by very high-energy flaring activity of M87. While inclusion of ALMA in the EHT is critical for shadow imaging, the array is generally robust against loss of a station.
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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 | 0.000 | 0.000 |
| 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".