Bibliographic record
Abstract
ABSTRACT In this paper, we carry out simulations of radial velocity (RV) measurements of the mass of the 8–11 Myr Neptune-sized planet K2-33b using high-precision near-infrared velocimeters like SPIRou at the Canada–France–Hawaii Telescope. We generate an RV curve containing a planet signature and a realistic stellar activity signal, computed for a central wavelength of 1.8 µm and statistically compatible with the light curve obtained with K2. The modelled activity signal includes the effect of time-evolving dark and bright surface features hosting a 2 kG radial magnetic field, resulting in an RV signal of semi-amplitude ∼30 m s−1. Assuming a 3-month visibility window, we build RV time series including Gaussian white noise from which we retrieve the planet mass while filtering the stellar activity signal using Gaussian process regression. We find that 35/50 visits spread over three consecutive bright-time runs on K2-33 allow one to reliably detect planet RV signatures of, respectively, 10 and 5 m s−1 at precisions >3σ. We also show that 30 visits may end up being insufficient in some cases to provide a good coverage of the stellar rotation cycle, with the result that the planet signature can go undetected or the mass estimation be plagued by large errors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".