Predictions of Planet Detections with Near-infrared Radial Velocities in the Upcoming SPIRou Legacy Survey-planet Search
Bibliographic record
Abstract
Abstract The SPIRou near-infrared spectropolarimeter is destined to begin science operations at the Canada–France–Hawaii Telescope in mid-2018. One of the instrument’s primary science goals is to discover the closest exoplanets to the solar system by conducting a three- to five-year long radial velocity survey of nearby M dwarfs at an expected precision of ∼1 m s−1, the SPIRou Legacy Survey-Planet Search (SLS-PS). In this study, we conduct a detailed Monte Carlo simulation of the SLS-PS using our current understanding of the occurrence rate of M dwarf planetary systems and physical models of stellar activity. From simultaneous modeling of planetary signals and activity, we predict the population of planets to be detected in the SLS-PS. With our fiducial survey strategy and expected instrument performance over a nominal survey length of ∼3 years, we expect SPIRou to detect planets including habitable-zone planets and Earth-like planets from a sample of 100 M1–M8.5 dwarfs out to 11 pc. By studying mid-to-late M dwarfs previously inaccessible to existing optical velocimeters, SPIRou will put meaningful constraints on the occurrence rate of planets around those stars including the value of at an expected level of precision of . We also predict that a subset of planets may be accessible with dedicated high-contrast imagers on the next generation of extremely large telescopes including potentially imagable Earth-like planets. Lastly, we compare the results of our fiducial survey strategy to other foreseeable survey versions to quantify which strategy is optimized to reach the SLS-PS science goals. The results of our simulations are made available to the community on GitHub ( https://github.com/r-cloutier/SLSPS_Simulations ).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".