Angular Differential Imaging: a Powerful High-Contrast Imaging Technique
Bibliographic record
Abstract
Angular differential imaging is a high-contrast imaging technique that reduces speckle noise from quasi-static optical aberrations and facilitates the detection of faint nearby companions. A sequence of images is acquired with an altitude/azimuth telescope, the instrument rotator being turned off. This keeps the instrument and telescope optics aligned, stabilizes the instrumental PSF and allows the field of view to rotate with respect to the instrument. For each image, a reference PSF obtained from other images of the sequence is subtracted. All residual images are then rotated to align the field and are median combined. Observed performances are reported for Gemini Altair/NIRI data. Inside the speckle dominated region of the PSF, it is shown that quasi-static PSF noise can be reduced by a factor {approx}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 images acquired. To our knowledge, this is the first time an acquisition strategy and reduction pipeline designed for speckle attenuation and high contrast imaging is demonstrated to significantly get better detection limits with longer integration times at all angular separations. A PSF noise attenuation of 100 was achieved from 2-hour long sequences of images of Vega, reaching a 5-sigma contrast of 20 magnitudes for separations greater than 7''. This technique can be used with currently available instruments to search for {approx} 1 M{sub Jup} 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".