Data reduction and calibration accuracy of the imaging Fourier transform spectrometer SITELLE
Bibliographic record
Abstract
ABSTRACT SITELLE, an imaging Fourier Transform Spectrometer, is part of the Canada–France–Hawaii instrument suite. It delivers spectral cubes covering an 11 arcmin × 11 arcmin field of view with a seeing-limited spatial resolution and a tunable spectral resolution (R ∼ 1–10 000) in selected bands of the visible range (350–900 nm). We present a complete picture of the calibration accuracy obtained with the SITELLE processing pipeline ORBS. We put a particular emphasis on the description of our phase correction method and on the assessment of the flux calibration precision. We show that the absolute flux calibration uncertainty is to be considered between −15 per cent and 5 per cent. Flexure in the instrument is likely responsible for a wavelength calibration error gradient across the field of view, with an amplitude corresponding to 15 to 25 km s−1; measurements of the night-sky emission lines when present in a science cube reduces this error to ∼2 km s−1. The astrometric calibration is limited to ∼1 arcsec by the optical distortions. Considering that imaging Fourier transform spectrometers are not as widely used as dispersive spectrometers and because SITELLE and its prototype are the first instruments of their kind to provide data in the near-UV at a high spectral resolution and over a very large field of view, we took great care in explaining most of the core concepts behind this technique as well as exploring all the practical limitations that affect the precision of our calibrations. As such, this paper aims at providing a solid ground for subsequent developments of imaging Fourier transform spectrometers in astronomy.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".