Efficient Speckle Noise Attenuation in Faint Companion Imaging
Bibliographic record
Abstract
Numerical simulations are used to evaluate a technique to attenuate speckle noise and enhance faint companion images buried in a bright‐star point‐spread function (PSF). It is shown that when frames taken simultaneously at two different wavelengths are subtracted from one another, the general evolution of the PSF profile with wavelength limits the attenuation to A 2 ∼2σ 2 ϕ Δλ/λ, where σ 2 ϕ is the wave‐front phase variance and Δλ the bandpass separation. When images taken at three wavelengths are combined in a double difference, the residuals caused by speckle evolution are very strongly damped and attenuations A 3 ∼A 2 2 are obtained. Very strong attenuation of speckle noise can thereby be achieved in images taken with adaptive optics (σ 2 ϕ <1). With three filters spanning the CH 4 bandhead at 1.59 μm in the spectrum of cool brown dwarfs or Jovian planets, speckle noise can be attenuated by a factor of ∼10 4 . This technique makes it possible to reach the photon limit in searches for methanated companions of even the brightest stars in the sky at separations of less than λ/D.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".