SPIRou: NIR velocimetry and spectropolarimetry at the CFHT
Bibliographic record
Abstract
ABSTRACT This paper presents an overview of SPIRou, the new-generation near-infrared spectropolarimeter/precision velocimeter recently installed on the 3.6-m Canada–France–Hawaii Telescope (CFHT). Starting from the two main science goals, i.e., the quest for planetary systems around nearby M dwarfs and the study of magnetized star/planet formation, we outline the instrument concept that was designed to efficiently address these forefront topics, and detail the in-lab and on-sky instrument performances measured throughout the intensive testing phase that SPIRou was submitted to before passing the final acceptance review in early 2019 and initiating science observations. With a central position among the newly started programmes, the SPIRou Legacy Survey (SLS) Large Programme was allocated 300 CFHT nights until at least mid 2022. We also briefly describe a few of the first results obtained in the various science topics that SPIRou started investigating, focusing in particular on planetary systems of nearby M dwarfs, transiting exoplanets and their atmospheres, magnetic fields of young stars, but also on alternate science goals like the atmospheres of M dwarfs and the Earth’s atmosphere. We finally conclude on the key role that SPIRou and the CFHT can play in coordination with forthcoming major facilities like the JWST, the ELTs, PLATO, and ARIEL over the decade.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".