NIRPS: the Near-InfraRed Planet Searcher joining HARPS on the 3.6-m
Bibliographic record
Abstract
The Near-InfraRed Planet Searcher (NIRPS) is designed to be an ultra-stable infrared spectrograph to be installed on ESO’s 3.6 m Telescope in La Silla, Chile. NIRPS is an adaptive optics (AO) fiber-fed spectrograph operating from 0.98 to 1.8 μm and will be operated simultaneously with the optical high-resolution spectrograph HARPS. NIRPS can operate in two modes fed by two different fiber links permanently mounted at the Cassegrain focus that use either 0.4 arcsecond-fibers for the High Accuracy Mode (HAM) or 0.9 arcsecond-fibers for the High Efficiency Mode (HEM). The wavelength range of NIRPS is optimal for low-mass M dwarfs and the simultaneous NIRPS and HARPS observations will improve stellar activity filtering methods given their different wavelength coverages. The NIRPS front-end and AO system were already tested on-sky at La Silla. The spectrograph and back-end is being shipped to La Silla and installed in Summer/Fall 2021. Already we have adapted the state-of-the-art ESPRESSO data reduction pipeline for NIRPS, obtained accurate wavelength solutions with a Uranium Neon lamp, and obtained drift stability results below 50 cm/s with a Fabry–Pérot etalon. We discuss the current and expected instrument performance and the expected results of NIRPS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".