Examining the Rotation Period Distribution of the 40 Myr Tucana–Horologium Association with TESS
Bibliographic record
Abstract
Abstract The Tucana–Horologium association (Tuc-Hor) is a 40 Myr old moving group in the southern sky. In this work, we measure the rotation periods of 313 Tuc-Hor objects with TESS light curves derived from TESS full-frame images and membership lists driven by Gaia EDR3 kinematics and known youth indicators. We recover a period for 81.4% of the sample and report 255 rotation periods for Tuc-Hor objects. From these objects we identify 11 candidate binaries based on multiple periodic signals or outlier Gaia DR2 and EDR3 renormalized unit weight error values. We also identify three new complex rotators (rapidly rotating M dwarf objects with intricate light-curve morphology) within our sample. Along with the six previously known complex rotators that belong to Tuc-Hor, we compare their light-curve morphology between TESS Cycle 1 and Cycle 3 and find that they change substantially. Furthermore, we provide context for the entire Tuc-Hor rotation sample by describing the rotation period distributions alongside other youth indicators such as Hα and Li equivalent width, as well as near-ultraviolet and X-ray flux. We find that measuring rotation periods with TESS is a fast and effective means to confirm members in young moving groups.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".