Performance of the Gemini Planet Imager Non-redundant Mask and Spectroscopy of Two Close-separation Binaries: HR 2690 and HD 142527
Bibliographic record
Abstract
Abstract The Gemini Planet Imager (GPI) contains a 10-hole non-redundant mask (NRM), enabling interferometric resolution in complement to its coronagraphic capabilities. The NRM operates both in spectroscopic (integral field spectrograph, henceforth IFS) and polarimetric configurations. NRM observations were taken between 2013 and 2016 to characterize its performance. Most observations were taken in spectroscopic mode, with the goal of obtaining precise astrometry and spectroscopy of faint companions to bright stars. We find a clear correlation between residual wavefront error measured by the adaptive optic system and the contrast sensitivity by comparing phase errors in observations of the same source, taken on different dates. We find a typical 5σ contrast sensitivity of (2–3) × 10−3 at ∼λ/D. We explore the accuracy of spectral extraction of secondary components of binary systems by recovering the signal from a simulated source injected into several data sets. We outline data reduction procedures unique to GPI’s IFS and describe a newly public data pipeline used for the presented analyses. We demonstrate recovery of astrometry and spectroscopy of two known companions to HR 2690 and HD 142527. NRM+polarimetry observations achieve differential visibility precision of σ ∼ 0.4% in the best case. We discuss its limitations on Gemini-S/GPI for resolving inner regions of protoplanetary disks and prospects for future upgrades. We summarize lessons learned in observing with NRM in spectroscopic and polarimetric modes.
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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) | 0.000 | 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 | 0.000 |
| 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".