Technical improvements and performances of SpIOMM: an imaging Fourier transform spectrometer for astronomy
Bibliographic record
Abstract
We present the most recent technical improvements on SpIOMM, an Imaging Fourier Transform Spectrometer (IFTS) attached to the 1.6 telescope of the Mont Mégantic Observatory. The recent development of SpIOMM demonstrates that the concept of IFTS for ground telescopes is a promising astronomical 3D spectroscopy technique for multi-object spectroscopy and multi-band imaging. SpIOMM has been developed through a collaboration between Université Laval and the industry (ABB Bomem). It is designed for optical observations from the near UV (350 nm) to the near IR (850 nm) with variable spectral resolution. The circular FOV of the instrument covers 12 arcmin in diameter. We have recently improved the servo system algorithm which now controls the mirror displacement and alignment at a rate of ~7000Hz. Hardware improvements to the servo and the metrology system will be described along with their impacts on performance in the laboratory and in observing conditions. The instrument has successfully been operated at the 1.6 meter telescope this year using the revised control systems and acquired several datacubes. We will discuss some issues regarding the sensitivity to environmental conditions implied by the use of such an instrument. An overview of the datacube reduction procedure will show some solutions proposed for observational problems encountered that affect the quality of the data such as sky transmission variations, wind, changing gravity vector and temperature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".