ON THE RADIAL VELOCITY DETECTION OF ADDITIONAL PLANETS IN TRANSITING, SLOWLY ROTATING M-DWARF SYSTEMS: THE CASE OF GJ 1132
Bibliographic record
Abstract
ABSTRACT M-dwarfs are known to commonly host high-multiplicity planetary systems. Therefore, M-dwarf planetary systems with a known transiting planet are expected to contain additional small planets (r p ≤ 4 R ⊕, m p ≲ 20 M ⊕) that are not seen in transit. In this study, we investigate the effort required to detect such planets using precision velocimetry around the sizable subset of M-dwarfs that are slowly rotating (P rot ≳ 40 days), and hence more likely to be inactive. We focus on the test case of GJ 1132. Specifically, we perform a suite of Monte-Carlo simulations of the star’s radial velocity signal, featuring astrophysical contributions from stellar jitter due to rotationally modulated active regions, as well as Keplerian signals from the known transiting planet and hypothetical additional planets not seen in transit. We then compute the detection completeness of non-transiting planets around GJ 1132 and consequently estimate the number of RV measurements required to detect those planets. We show that, with 1 m s−1 precision per measurement, only ∼50 measurements are required to achieve a 50% detection completeness for all non-transiting planets in the system, as well as planets that are potentially habitable. Throughout this work, we advocate the use of Gaussian process regression as an effective tool for mitigating the effects of stellar jitter including stars with high activity. Given that GJ 1132 is representative of a large population of slowly rotating M-dwarfs, we conclude with a discussion of how our results may be extended to other systems with known transiting planets, such as those that will be discovered with TESS.
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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) | 0.001 | 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 | 0.001 |
| 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".