Adventures with SPIRou: The APERO pipeline and LBL RV analysis tool
Bibliographic record
Abstract
Data reduction (pipelines and analysis software) is a critical and necessary component required to go from observations to scientific results. With the maturation of near-infrared high-resolution spectroscopy, especially when used for precision radial velocity, data reduction has been faced with unprecedented challenges in terms of how one goes from raw data to clean, calibrated, extracted, and corrected data with required precision of thousandths of a pixel. Here we present APERO and LBL. APERO is a pipeline designed to reduce observations, specifically focused on our first instrument, SPIRou, the near-infrared spectropolarimeter on the Canadian France Hawaii Telescope. The line-by-line (LBL) method of pRV measurements is designed to be outlier-resistant while taking advantage of the full RV content of the input spectra. The method splits high-resolution spectra into thousands of absorption lines for which it measures individual velocities. These velocities are combined through a simple finite mixture model that accounts for the likelihood that a given line may be an outlier due to telluric absorption or other observational biases. The combination of APERO and LBL with SPIRou leads to m/s precisions in radial velocity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.038 |
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".