Scientific detector workshop 2022 on‐sky performance verification of near‐infrared <scp>e<sup>−</sup>APD</scp> technology for wavefront sensing and demonstration of <scp>e<sup>−</sup>APD</scp> pixel performance to improve the sensitivity of large science focal planes
Bibliographic record
Abstract
Abstract Near‐infrared adaptive optics as well as fringe tracking for coherent beam combination in optical interferometry require the development of high‐speed sensors. Because of the high speed, a large analog bandwidth is required. The short exposure times result in small signal levels which require noiseless detection. Both requirements cannot be met by state‐of‐the‐art conventional CMOS technology of near‐infrared arrays as has been attempted previously. A total of five near‐infrared SAPHIRA 320 × 256 pixel HgCdTe e−APD arrays have been deployed in the wavefront sensors and in the fringe tracker of the VLTI instrument GRAVITY. The current limiting magnitude for coherent exposures with GRAVITY is mk = 19, which is made possible with ADP technology. New avalanche photo‐diode array (APD) developments since GRAVITY include the extension of the spectral sensitivity to the wavelength range from 0.8 to 2.5 μm. After GRAVITY a larger format array with 512 × 512 pixels has been developed for both AO applications at the ELT and for long integration times. Since dark currents of <10−3 e−/s have been demonstrated with 1Kx1K e−APD arrays and 2Kx2K e−APD arrays have already been developed, the possibilities and adaptations of e−APD technology to provide noiseless large‐format science‐grade arrays for long integration times are also discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.021 |
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".