SNDICE: a direct illumination calibration experiment at CFHT
Bibliographic record
Abstract
We present the first results of the SuperNova Direct Illumination Calibration Experiment (SNDICE), installed in January 2008 at the Canada France Hawaii Telescope. SNDICE is designed for the absolute calibration of the instrumental response of a telescope in general, and for the control of systematic errors in the SuperNova Legacy Survey (SNLS) on Megacam in particular. Since photometric calibration will a critical ingredient for the cosmological results of future experiments involving instruments with large focal planes (like SNAP, LSST and DUNE), SNDICE functions also as a real-size demonstrator for such a system of instrumental calibration. SNDICE includes a calibrated source of 24 LEDs, chosen for their stability, spectral coverage, and their power, sufficient for a flux of at least 100 electron/s/pixel on the camera. It includes also Cooled Large Area Photodiode modules (CLAPs), which give a redundant measurement of the flux near the camera focal plane. Before installing SNDICE on CFHT, we completed a full calibration of both subsystems, including a spectral relative calibration and a 3D mapping of the beam emitted by each LED. At CFHT, SNDICE can be operated both to obtain a complete one-shot absolute calibration of telescope transmission in all wavelengths for all filters with several incident angles, and to monitor variations on different time scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".