Nancy Grace Roman Space Telescope coronagraph EMCCD flight camera electronics development
Bibliographic record
Abstract
The Nancy Grace Roman Space Telescope Coronagraph is a JPL-led space-based instrument that will be the most sensitive instrument ever built for direct imaging and characterization of exoplanets in the visible. The instrument contrast is expected to be better than 1e-9, which implies that it will be capable of seeing exoplanets with an apparent magnitude < 30. With such a low brightness, only a few photons per hour will be perceived by its optical detectors. Two cameras will be used on the instrument for wavefront sensing, direct imaging and spectroscopy, with frame rates ranging from 1000 fps to less than 0.01 fps. For such a broad range of operating modes and industry leading noise figure, JPL has selected the 1024x1024 CCD201- 20 EMCCD from Teledyne-e2v as the image sensor for the two coronagraph cameras and appealed to Nüvü Caméras to adapt its most recent space controller design for the mission specifics. The new version of the camera readout electronics co-developed with ABB Space System group brings important improvements over the version flown at the edge of space in CSA’s 2018 STRATOS campaign namely on reliability, functionality, thermal control, power, volume and mass whilst preserving its unique noise performance. This paper presents an overview of the project and addresses the development of the delivered flight modules.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".