SOAR TESS Survey. I. Sculpting of TESS Planetary Systems by Stellar Companions
Bibliographic record
Abstract
Abstract The Transiting Exoplanet Survey Satellite ( TESS ) is finding transiting planet candidates around bright, nearby stars across the entire sky. The large field of view, however, results in low spatial resolution; therefore, multiple stars contribute to almost every TESS light curve. High angular resolution imaging can detect the previously unknown companions to planetary candidate hosts that dilute the transit depths, lead to host star ambiguity, and, in some cases, are the source of false-positive transit signals. We use speckle imaging on the Southern Astrophysical Research (SOAR) telescope to search for companions to 542 TESS planet candidate hosts in the southern sky. We provide correction factors for the 117 systems with resolved companions due to photometric contamination. The contamination in TESS due to close binaries is similar to that found in surveys of Kepler planet candidates. For the solar-type population, we find a deep deficit of close binary systems with projected stellar separations less than 100 au among planet candidate hosts (44 observed binaries compared to 124 expected based on field binary statistics). The close binary suppression among TESS planet candidate hosts is similar to that seen for the more distant Kepler population. We also find a large surplus of TESS planet candidates in wide binary systems detected in both SOAR and Gaia DR2 (119 observed binaries compared to 77 expected). These wide binaries almost exclusively host giant planets, however, suggesting that orbital migration caused by perturbations from the stellar companion may lead to planet–planet scattering and suppress the population of small planets in wide binaries. Both trends are also apparent in the M dwarf planet candidate hosts.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".