Angular Differential Imaging: A Powerful High‐Contrast Imaging Technique
Why this work is in the frame
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Bibliographic record
Abstract
Angular differential imaging is a high-contrast imaging technique that reduces quasi-static speckle noise and facilitates the detection of nearby companions. A sequence of images is acquired with an altitude/azimuth telescope while the instrument field derotator is switched off. This keeps the instrument and telescope optics aligned and allows the field of view to rotate with respect to the instrument. For each image, a reference PSF is constructed from other appropriately-selected images of the same sequence and subtracted to remove quasi-static PSF structure. All residual images are then rotated to align the field and are combined. Observed performances are reported for Gemini North data. It is shown that quasi-static PSF noise can be reduced by a factor \~5 for each image subtraction. The combination of all residuals then provides an additional gain of the order of the square root of the total number of acquired images. A total speckle noise attenuation of 20-50 is obtained for one-hour long observing sequences compared to a single 30s exposure. A PSF noise attenuation of 100 was achieved for two-hour long sequences of images of Vega, reaching a 5-sigma contrast of 20 magnitudes for separations greater than 8". For a 30-minute long sequence, ADI achieves 30 times better signal-to-noise than a classical observation technique. The ADI technique can be used with currently available instruments to search for ~1MJup exoplanets with orbits of radii between 50 and 300 AU around nearby young stars. The possibility of combining the technique with other high-contrast imaging methods is briefly discussed.
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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 | 0.001 |
| 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 it