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Record W4404100658 · doi:10.1051/0004-6361/202451537

Exoplanets across galactic stellar populations with PLATO

2024· article· en· W4404100658 on OpenAlexfundno aff
C. Boettner, Anand Viswanathan, Pratika Dayal

Bibliographic record

VenueAstronomy and Astrophysics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryUniversity of Colorado BoulderInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMinistério da Ciência, Tecnologia e InovaçãoNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of OxfordYork UniversityLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityUniversidad Nacional Autónoma de MéxicoAlfred P. Sloan FoundationUniversity of WashingtonEuropean Space AgencyJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahAustralian Astronomical Optics-MacquarieRijksuniversiteit GroningenOhio State UniversityU.S. Department of EnergySmithsonian InstitutionNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityMax-Planck-Institut für AstrophysikEuropean Commission
KeywordsPhysicsExoplanetAstrophysicsHaloStarsThin diskThick diskGalactic haloAstronomyDiscGalaxyGalaxy formation and evolution

Abstract

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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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.017
GPT teacher head0.284
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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