Gemini Planet Imager observational calibrations XI: pipeline improvements and enhanced calibrations after two years on sky
Bibliographic record
Abstract
The Gemini Planet Imager has been successfully obtaining images and spectra of exoplanets, brown dwarfs, and debris and protoplanetary circumstellar disks using its integral field spectrograph and polarimeter. GPI observations are transformed from raw data into high-quality astrometrically and photometrically calibrated datacubes using the GPI Data Reduction Pipeline, an open-source software framework continuously developed by our team and available to the community. It uses a flexible system of reduction recipes composed of individual primitive steps, allowing substantial customization of processing depending upon science goals. This paper provides a broad overview of the GPI pipeline, summarizes key lessons learned, and describes improved calibration methods and new capabilities available in the latest version. Enhanced automation better supports observations at the telescope with streamlined and rapid data processing, for instance through real-time assessments of contrast performance and more automated calibration file processing. We have also incorporated the GPI Data Reduction Pipeline as one component in a larger automated data system to support the GPI Exoplanet Survey campaign, while retaining its flexibility and stand-alone capabilities to support the broader GPI observer community. Several accompanying papers describe in more detail specific aspects of the calibration of GPI data in both spectral 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".