Recherche de compagnons de type Jupiter à très grandes séparations autour d’étoiles jeunes dans le voisinage solaire
Bibliographic record
Abstract
The main goal of this PhD thesis is to search for exoplanets on wide orbits around young stars using the direct-imaging technique. These exoplanets are of high interest for several reasons. Firstly, as they are far from their host star, they can be studied as if they were isolated objects without the need for sophisticated imaging and data analysis techniques. In some cases, a high resolution spectrum can be acquired to learn more about them. Also, contrary to isolated objects of similar masses, their age and distance can be easily inferred from those of their host star. It is thus interesting to probe around stars for which those characteristics are well known, as they are crucial to infer the masses of exoplanets. As a first step, the results from a search for giant planets on wide orbits are described. A sample of 177 stars member of known young associations inside 70 pc of the Sun were observed using the Canada-France-Hawaii telescope, the Gemini-Sud observatory and the space telescope Spitzer. The observations reached a good completeness down to masses as low as 2 MJup at separations between 1000 and 5000 AU. Four candidate planets were detected, but they were identified as background objects using follow-up observations. A frequency of planets per star was inferred from the survey, such that less than 3% of the stars have at least one planet with masses 1–13MJup at separations of 1000–5000 AU, with a 95% confidence level. The next step was to combine several archival direct-imaging surveys to the study presented above. A sample of 344 unique stars all confirmed members of young associations was obtained. A Bayesian and Markov chain Monte Carlo analysis was realised to constrain the frequency of companions as well as the distribution of giant planets with masses between 1 and 20 MJup at separations 5-5000 AU. It was inferred from this analysis that 2.17+6.85−1.40%, 0.3+2.6 −0.1%, and 2.61+6.97−1.00% of stars have at least a planet of mass 1-20MJup at separations of 20–1000 AU, 1000–5000 AU and 20–5000 AU, respectively. Furthermore, assuming that the mass and orbital distribution of giant planets follows a power-law such as d2n ∝ fM^αa^βdMda, the parameters can be constrained to α = −0.08+0.75−0.63 and β = −1.41+0.22−0.24, with a 68% confidence level, with a corresponding planetary fraction per stars f = 0.12+0.11−0.06. A dependency over the mass of the host star was then added to the distribution such as d2n ∝ M^αa^β(M⋆/M⊙)^γdMda. In this case, α = −0.18+0.77−0.65, β = −1.43+0.23−0.24, γ = 0.62+0.56−0.50 and f = 0.11+0.11−0.05, with a 68% confidence level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".