Coherent differential imaging: squeezing additional imaging contrast behind the self-coherent camera on SPIDERS
Bibliographic record
Abstract
Coherent Differential Imaging (CDI) is a very promising post-processing technique for imaging faint planets and disks with a self-coherent camera (SCC). The SCC we have built in the Subaru Pathfinder Instrument for Detecting Exoplanets and Recovering Spectra (SPIDERS) employs common path interferometry to distinguish coherent starlight from incoherent planet/disk light. Using closed-loop real-time correction, the SCC greatly improves the achievable imaging contrast. The CDI technique is applied during image post-processing to further remove residual speckle artifacts that were not fully corrected by the SSC loop, deepening the imaging contrast. In this proceeding we describe the procedures developed to carry out CDI corrections, as well as the technical challenges encountered and solutions implemented along the way. The main findings thus far are that chromatic blurring of fringes is causing the strongest limitations on our CDI speckle removal. Chromatic amplitude corrections to the constructed reference image show improvement, but more in-depth modelling or deconvolution of blurring effects are needed. Our CDI recipe produces a factor of ∼15× improvement in the core of the PSF. We expect to push this improvement to 20 to 30× with more advanced chromatic treatment, and extend the improvement to wider separations.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".