Bibliographic record
Abstract
METIS is the Mid-infrared E-ELT Imager and Spectrograph, one of three first generation instruments of the European Extremly Large Telescope (E-ELT). The primary mirror of the E-ELT has a diameter of 39 meter. This huge aperture requires an adaptive optics system in order to provide the performance close to the diffraction limit that is needed for all scientific observing modes of METIS. METIS will be be equipped with a single conjugated adaptive optics (SCAO) system that makes use of a near-infrared wavefront sensor. A real-time computer (RTC) reconstructs the wavefront and sends correction commands to the deformable mirror M4 and the field stabilization mirror M5. The METIS SCAO team evaluates computer workstations with the many-core processor Intel® Xeon Phi™ Knights Landing (KNL) for the real-time computation within the wavefront control loop. This processor is a promising candidate to satisfy the METIS AO needs as it allows for massively parallel computations. This parallelization is achieved by utilizing up to 272 logical CPUs, the new SIMD (single instruction, multiple data) instruction set AVX-512 and the high-bandwidth on-CPU memory MCDRAM. This paper describes the SCAO RTC concept, evaluates the computational performance that a wavefront control loop could have when using a Xeon Phi KNL, and compares it to the identified requirements. The measurements will be conducted with the AO system software of the LINC-NIRVANA project under Linux. Especially, the real-time capability is analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.020 |
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".