Data reduction software for the Mid-Infrared E-ELT Imager and Spectrograph (METIS) for the European Extremely Large Telescope (E-ELT)
Bibliographic record
Abstract
We present the current status of the design of the science data reduction pipeline and the corresponding dataflow system for METIS. It will be one of the first three instruments for the E-ELT and work at wavelengths between 3-19 μm (L/M/N/Q1 bands). We will deliver software which is compliant to standards of the European Southern Observatory (ESO), and will employ state of the art techniques to produce science grade data, master calibration frames, quality control parameters and to handle instrument effects. The Instrument currently offers a wealth of observing modes that are listed in this paper. Data reduction for a ground based instrument at these wavelengths is particularly challenging because of the massive influence of thermal radiation from various sources. We will give a comprehensive overview of the data ow system for the imaging modes that the instrument offers and discuss a single recipe versus a multi recipe approach for the different observing modes for imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".