Temporal Evolution of Coronagraphic Dynamic Range and Constraints on Companions to Vega
Bibliographic record
Abstract
The major obstacle to the direct detection of companions to nearby stars is the overwhelming brightness of the host star. Current instruments employing the combination of adaptive optics (AO) and coronagraphy can typically detect objects within 2'' of the star that are ~10 4 -10 5 times fainter. Correlated speckle noise is one of the biggest obstacles limiting such high-contrast imaging. We have obtained a series of 284 8 s, AO-corrected, coronagraphically occulted H -band images of the star Vega at the 3.63 m AEOS telescope located on Haleakala, Hawaii. This data set is unique for studying the temporal behavior of speckle noise and represents the first time such a study on highly corrected coronagraphic AO images has been carried out in a quantitative way. We find the speckle pattern to be highly stable in both position and time in our data. This is due to the fact that the AO system corrects disturbances to the stellar wave front at the level where the instrumental wave front errors dominate. Because of this, we find that our detection limit is not significantly improved simply with increased exposure time alone. However, we are able to improve our dynamic range by 1.5-2 mag through subtraction of static/quasi-static speckles in two rotating frames: the telescope pupil frame and the deformable mirror frame. The highly stable nature of speckles will exist for any program using coronagraphy and high-order AO. Furthermore, from our data, we are able to constrain the mass of any purported companion to Vega to be less than ~45 M J at 8 AU and less than ~30 M J at 16 AU, radii not previously probed at these sensitivities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".