Diving into the planetary system of Proxima with NIRPS
Bibliographic record
Abstract
We obtained 420 high-resolution spectra of Proxima, over 159 nights, using the Near Infra Red Planet Searcher (NIRPS). We derived 149 nightly binned radial velocity measurements with a standard deviation of 1.69 ms −1 and a median uncertainty of 55 cms −1 , and performed a joint analysis combining radial velocities, spectroscopic activity indicators, and ground-based photometry, to model the planetary and stellar signals present in the data, applying multi-dimensional Gaussian process regression to model the activity signals. We detect the radial velocity signal of Proxima b in the NIRPS data. All planetary characteristics are consistent with those previously derived using visible light spectrographs. In addition, we find evidence of the presence of the sub-Earth Proxima d in the NIRPS data. When combining the data with the HARPS observations taken simultaneous to NIRPS, we obtain a tentative detection of Proxima d and parameters consistent with those measured with ESPRESSO. By combining the NIRPS data with simultaneously obtained HARPS observations and archival data, we confirm the existence of Proxima d, and demonstrate that its parameters are stable over time and against change of instrument. We refine the planetary parameters of Proxima b and d, and find inconclusive evidence of the signal attributed to Proxima c (P = 1900 d) being present in the data. We measure Proxima b and d to have minimum masses of 1.055 ± 0.055 M ⊕ , and 0.260 ± 0.038 M ⊕ , respectively. Our results show that, in the case of Proxima, NIRPS provides more precise radial velocity data than HARPS, and a more significant detection of the planetary signals. The standard deviation of the residuals of NIRPS after the fit is ~80 cm s −1 , showcasing the potential of NIRPS to measure precise radial velocities in the near-infrared.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".