Upgrading the Gemini planet imager to GPI 2.0
Bibliographic record
Abstract
The Gemini Planet Imager (GPI) is a high-contrast imaging instrument designed to directly detect and characterize young, Jupiter-mass exoplanets. After six years of operation at Gemini South in Chile, the instrument is being upgraded and relocated to Gemini North in Hawaii as GPI 2.0. GPI helped establish that Jovian-mass planets have a higher occurrence rate at smaller separations, motivating several sub-system upgrades to obtain deeper contrasts (up to 20 times improvement to the current limit), particularly at small inner working angles. This enables access to additional science areas for GPI 2.0, including low-mass stars, young nearby stars, solar system objects, planet formation in disks, and planet variability. The necessary instrumental changes required toenable these new scientific goals are to (i) the adaptive optics system, by replacing the current Shack-Hartmann Wavefront Sensor (WFS) with a pyramid WFS and a custom EMCCD, (ii) the integral field spectrograph, by employing a new set of prisms to enable an additional broadband (Y-K band) low spectral resolution mode, as well as replacing the pupil viewer camera with a faster, lower noise C-RED2 camera (iii) the calibration interferometer, by upgrading the low-order WFS used for internal alignment and on-sky target tracking with a C-RED2 camera and replacing the calibration high-order WFS used for measuring and correcting non-common path aberrations with a self coherent camera, (iv) the apodized-pupil Lyot coronagraph designs and (v) the software, to enable high-efficiency queue operations at Gemini North. GPI 2.0 is expected to go on-sky in early 2024. Here I will present the new scientific goals, the key upgrades, the current status and the latest timeline for operations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".