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Record W4409256997 · doi:10.1051/0004-6361/202453059

Ariel stellar characterisation

2025· article· en· W4409256997 on OpenAlexfundno aff
Tsantaki, Magrini, Danielski, Bossini, Moedas, Folsom, Ramler, H., Biazzo, K.

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInstitut sur la Nutrition et les Aliments FonctionnelsEuropean CommissionAgencia Estatal de InvestigaciónNational Science Foundation
KeywordsPhysicsAstrophysicsStarsAstronomySample (material)

Abstract

fetched live from OpenAlex

Context . The next mission dedicated to the study of planetary atmospheres is the Ariel space mission, planned for launch in 2029, which will observe a variety of planetary systems belonging to different classes around stars with spectral types from M to A . To optimise the scientific outcome of the mission, such stars need to be homogeneously characterised beforehand. Aims . In this work, we focus on a methodology based on spectral synthesis for the characterisation of FGK-type stars from the Ariel Tier 1 mission candidate sample (MCS) that exhibit fast rotation. In addition, we analyse 108 slow-rotating FGK-type stars, with either new observations or archival spectra available, consistently as in our previous work using the equivalent width (EW) analysis. Methods . To ensure consistency between our methods, we re-analysed a sample of FGK-type stars with the spectral synthesis method and compared it to our previous work. The results of our analysis show excellent agreement with the previous set of derived parameters. Results . We provide homogeneous effective temperature, surface gravity, metallicity, projected rotational velocity, and stellar mass for a sample of 36 fast rotators with the spectral synthesis technique, and we include 108 FGK-type dwarfs with the EW analysis. An additional 25 stars were analysed with the spectral synthesis method because their EW analysis did not converge on the final parameters. We computed their orbital parameters establishing whether they belong to the Galactic thin or thick discs. With the current set of stellar parameters, we almost double the analysed hosts in the Ariel MCS to 353 stars in total. Conclusions . Using our homogeneous set of stellar parameters, we studied the correlations between stellar and planetary properties for the Ariel MCS analysed so far. We confirmed a close relationship between stellar mass (up to 1.8 M ⊙ ) and giant planet radius, with more inflated planets at lower metallicity. We confirm that giant planets are more frequent around more metal-rich stars that belong to the thin disc, while lower-mass planets are also found in more metal-poor environments, and are more frequent than giant planets in the thick disc as also seen in other works in the literature.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.007
GPT teacher head0.245
Teacher spread0.238 · 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 teacher head, not a consensus.

Study designOther design
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

Citations7
Published2025
Admission routes1
Has abstractyes

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