AQUARIUS, the next generation mid-IR detector for ground-based astronomy
Bibliographic record
Abstract
ESO has recently funded the development of the AQUARIUS detector at Raytheon Vision Systems, a new mega-pixel Si:As Impurity Band Conduction array for use in ground based astronomical applications at wavelengths between 3 – 28 μm. The array has been designed to have low noise, low dark current, switchable gain and be read out at very high frame rates. It has 64 individual outputs capable of pixel read rates of 3MHz, implying continuous data-rates in excess of 300 Mbytes/second. It is scheduled for deployment into the VISIR instrument at the VLT in 2012, for next generation VLTI instruments and base-lined for METIS, the mid-IR candidate instrument for the E-ELT. A new mid-IR test facility has been developed for AQUARIUS detector development which includes a low thermal background cryostat, high speed cryogenic pre-amplification and high speed data acquisition and detector operation at 5K. We report on all the major performance aspects of this new detector including conversion gain, read noise, dark generation rate, linearity, well capacity, pixel operability, low frequency noise, persistence and electrical cross-talk. We describe the many possible readout modes of this detector and their application. We also report on external issues with the operation of these detectors at such low temperatures. Finally we report on the electronic developments required to operate such a detector at the required high data rates and in a typical mid-IR instrument.
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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 | 0.001 | 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) | 0.009 | 0.003 |
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".