DRAGraces: A Pipeline for the GRACES High-resolution Spectrograph at Gemini*
Bibliographic record
Abstract
Abstract This paper describes the software Data Reduction and Analysis for GRACES (DRAGraces), which is a pipeline reducing spectra from Gemini Remote Access to the CFHT ESPaDOnS Spectrograph (GRACES) at the Gemini North Telescope. The code is written in the IDL language. It is designed to find all the GRACES frames in a given directory, automatically determine the list of bias, flat, arc, and science frames, and perform the whole reduction and extraction within a few minutes. We compare the output from DRAGraces with that of the Open source Pipeline for ESPaDOnS Reduction and Analysis (OPERA), a pipeline developed at the Canada–France–Hawaii Telescope (CFHT) that also can extract GRACES spectra. Both pipelines were developed completely independently, yet they give very similar extracted spectra. They both have their advantages and disadvantages. For instance, DRAGraces is more straightforward and easy to use and is less likely to be derailed by a parameter that needs to be tweaked, while OPERA offers a more careful extraction that can be significantly superior when the highest resolution is required and when the signal-to-noise ratio is low. One should compare both before deciding which one to use for their science. Yet, both pipelines deliver a fairly comparable resolution power (R ∼ 52.8k and 36.6k for DRAGraces and R ∼ 58k and 40k for OPERA in high- and low-resolution spectral modes, respectively), wavelength solution, and signal-to-noise ratio per resolution element.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".