Efficiently Searching for Close-in Companions Around Young M Dwarfs Using a Multiyear PSF Library
Bibliographic record
Abstract
Abstract We present Super-RDI, a unique framework for the application of reference star differential imaging (RDI) to Keck/NIRC2 high-contrast imaging observations with the vortex coronagraph. Super-RDI combines frame selection and signal-to-noise ratio (S/N) optimization techniques with a large multiyear reference point-spread function (PSF) library to achieve optimal PSF subtraction at small angular separations. We compile an ∼7000 frame reference PSF library based on a set of 288 new Keck/NIRC2 L ′ sequences of 237 unique targets acquired between 2015 and 2019 as part of two planet-search programs designed for RDI, one focusing on nearby young M dwarfs and the other targeting members of the Taurus star-forming region. For our data set, synthetic companion injection-recovery tests reveal that frame selection with the mean-squared error metric combined with Karhunen–Loève Image-Processing-based PSF subtraction using 1000–3000 frames and ≲500 principal components yields the highest average S/N for injected synthetic companions. We uniformly reduce targets in the young M-star survey with both Super-RDI and angular differential imaging (ADI). For the typical parallactic angle rotation of our data set (∼10°), Super-RDI performs better than a widely used implementation of ADI-based PSF subtraction at separations ≲0.″4 (≈5 λ/D), gaining an average of 0.25 mag in contrast at 0.″25 and 0.4 mag in contrast at 0.″15. This represents a performance improvement in separation space over RDI with single-night reference star observations (∼100 frame PSF libraries) applied to a similar Keck/NIRC2 data set in previous work. We recover two known brown dwarf companions and provide detection limits for 155 targets in the young M-star survey. Our results demonstrate that increasing the PSF library size with careful selection of reference frames can improve the performance of RDI with the Keck/NIRC2 vortex coronagraph in L ′ .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".