Exoplanet imaging with LOCI processing: photometry and astrometry with the new SOSIE pipeline
Bibliographic record
Abstract
The Angular, Simultaneous Spectral and Reference Star Differential Imaging techniques (ADI, SSDI and RSDI) are currently the main observing approaches that are being used to pursue large-scale direct exoplanet imaging surveys and will be a key component of next-generation high-contrast imaging instrument science. To allow detection of faint planets, images from these observing techniques are combined in a way to retain the planet flux while subtracting as much as possible the residual speckle noise. The LOCI algorithm is a very efficient way of combining a set of reference images to subtract the noise of a given image. Although high contrast performances have been achieved with ADI/SSDI/RSDI & LOCI, achieving high accuracy photometry and astrometry can be a challenge, due to various biases coming mainly from the inevitable partial point source self-subtraction for ADI/SSDI and how LOCI is designed to suppress the noise. We present here several biases that we hare uncovered while analyzing data on the HR8799 planetary system and how we have modified our analysis pipeline to calibrate or remove these effects so that high accuracy astrometry and photometry is achievable. In addition, several new upgrades are presented in a new archive-based (i.e. performing ADI, SSDI and RSDI with LOCI as a single PSF subtraction step) multi-instrument reduction and analysis pipeline called SOSIE.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".