Exoplanets across galactic stellar populations with PLATO
Bibliographic record
Abstract
Context . The vast majority of exoplanet discoveries to date have occurred around stars in the solar neighbourhood, with chemical compositions comparable to that of the Sun. However, models suggest that planetary systems in different Galactic environments, with varying dynamical histories and chemical abundances, may exhibit distinct characteristics, which can help improve our understanding of planet formation processes. Aims . This study aims to assess the potential of the upcoming PLATO mission to investigate exoplanet populations around stars in diverse Galactic environments, specifically focusing on the Milky Way thin disk, thick disk, and stellar halo. We aim to quantify PLATO’s ability to detect planets in each environment and determine how these observations could constrain planet formation models. Methods . Beginning with the all-sky PLATO Input Catalogue, we kinematically classified the 2.4 million FGK stars into their respective Galactic components. For the sub-sample of stars in the long-observation LOPS2 and LOPN1 PLATO fields, we estimated planet occurrence rates using the New Generation Planet Population Synthesis dataset. Combining these estimates with a PLATO detection efficiency model, we predicted the expected planet yields for each Galactic environment during a nominal 2+2 year mission. Results . Based on our analysis, PLATO is likely to detect at least 400 exoplanets around the α -enriched thick disk stars. The majority of those planets are expected to be super-Earths and sub-Neptunes with radii between 2 and 10 R ⊕ and orbital periods between 2 and 50 days, which is ideal for studying the link between the radius valley and stellar chemistry. For the metal-poor halo, PLATO is likely to detect between 1 and 80 planets with periods between 10 and 50 days, depending on the potential existence of a metallicity threshold for planet formation. The PLATO fields contain more than 3400 potential target stars with [Fe/H] < −0.6, which will help improve our understanding of planets around metal-poor stars. We identified a specific target list of 47 (kinematically classified) halo stars in the high-priority, high-signal-to-noise PLATO P1 sample, offering prime opportunities in the search for planets in metal-poor environments. Conclusions . PLATO’s unique capabilities and large field of view position it as a valuable tool for studying planet formation across the diverse Galactic environments of the Milky Way. By probing exoplanet populations around stars with a varying chemical composition, PLATO will provide helpful insights into the connection between stellar chemistry and planet formation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".