A PSF-based Approach to <i>TESS</i> High quality data Of Stellar clusters (PATHOS) – III. Exploring the properties of young associations through their variables, <i>dippers</i>, and candidate exoplanets
Bibliographic record
Abstract
ABSTRACT Young associations in star-forming regions are stellar systems that allow us to understand the mechanisms that characterize the stars in their early life and what happens around them. In particular, the analysis of the discs and of the exoplanets around young stars allows us to know the key processes that prevail in their evolution and understand the properties of the exoplanets orbiting older stars. The Transiting Exoplanet Survey Satellite mission is giving us the opportunity to extract and analyse the light curves of association members with high accuracy, but the crowding that affects these regions makes difficult the light curve extraction. In the PATHOS project, cutting-edge tools are used to extract high-precision light curves and identify variable stars and transiting exoplanets in open clusters and associations. In this work, I analysed the light curves of stars in five young (≲10 Myr) associations, searching for variables and candidate exoplanets. Using the rotational periods of the association members, I constrained the ages of the five stellar systems (∼2–10 Myr). I searched for dippers, and I investigated the properties of the dust that forms the circumstellar discs. Finally, I searched for transiting signals, finding six strong candidate exoplanets. No candidates with radius RP ≲ 0.9 RJ have been detected, in agreement with the expectations. The frequency of giant planets resulted to be ∼2–3 per cent, higher than that expected for field stars (≲ 1 per cent); the low statistic makes this conclusion not strong, and new investigations on young objects are mandatory to confirm this result.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".