ORBS: A data reduction software for the imaging Fourier transform spectrometers SpIOMM and SITELLE
Bibliographic record
Abstract
SpIOMM (Spectromètre-Imageur de l'Observatoire du Mont Mégantic) is still the only operational astronomical Imaging Fourier Transform Spectrometer (IFTS) capable of obtaining the visible spectrum of every source of light in a field of view of 12 arc-minutes. Even if it has been designed to work with both outputs of the Michelson interferometer, up to now only one output has been used. Here we present ORBS (Outils de Réduction Binoculaire pour SpIOMM/SITELLE), the reduction software we designed in order to take advantage of the two output data. ORBS will also be used to reduce the data of SITELLE (Spectromètre-Imageur pour l' Étude en Long et en Large des raies d' Émissions) { the direct successor of SpIOMM, which will be in operation at the Canada-France- Hawaii Telescope (CFHT) in early 2013. SITELLE will deliver larger data cubes than SpIOMM (up to 2 cubes of 34 Go each). We thus have made a strong effort in optimizing its performance efficiency in terms of speed and memory usage in order to ensure the best compliance with the quality characteristics discussed with the CFHT team. As a result ORBS is now capable of reducing 68 Go of data in less than 20 hours using only 5 Go of random-access memory (RAM).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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".