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^{4-5} 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 dataset 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 magnitudes through subtraction of static/quasi-static speckles in two rotating frames: the telescope pupil frame and the deformable mirror frame. 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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".