Infrared detectors for first generation extremely large telescope instruments and their characterization program
Bibliographic record
Abstract
The detectors for the three first generation extremely large telescope (ELT) instruments MICADO (Multi‐AO Imaging Camera for Deep Observations), HARMONI (A High Angular Resolution Monolithic Optical and Near‐infrared Integral Field Spectrograph), and METIS (A Mid‐infrared ELT Imager and Spectrograph) cover from the optical to longwave infrared wavelengths. MICADO and HARMONI detector focal planes require 17 H4RG (Hawaii‐4RG) near‐infrared whilst the METIS focal planes consist of 5 SWIR (shortwave infrared) detectors and one longwave infrared detector. Procurement of the optical and SWIR detectors has been completed, whilst the H4RGs and long wavelength detectors are still in progress. A custom cryogenic facility for infrared array testing (FIAT) has been designed and developed at European Southern Observatory (ESO) over the last few years in order to characterize the infrared detectors for ELT and future Very Large Telescope instruments. FIAT is currently being commissioned in our labs with an H4RG engineering detector. FIAT will be used to characterize the science H4RG‐15 detectors for MICADO and HARMONI, the two first‐light instruments of the ELT while the existing Mosaic Test Facility (MTF) will be used for characterizing the SWIR detectors for METIS. This paper presents an overview of the detector systems for the three instruments, their engineering challenges, and the requirements for the detectors' performance and their characterization program. The paper will also describe a test setup for H4RG detectors including a new preamplifier design with options to operate the detector in different modes and it will also report on the test results from the engineering H4RG detector, as part of the commissioning of FIAT and discuss detector performance and related detector issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".