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Record W2735678678 · doi:10.1051/0004-6361/201731236

Influence of inelastic collisions with hydrogen atoms on the non-LTE modelling of Ca i and Ca ii lines in late-type stars

2017· article· en· W2735678678 on OpenAlexfundno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsStarsMetallicityAbundance (ecology)Line (geometry)IonizationThermodynamic equilibriumStellar atmosphereSpectral lineIon

Abstract

fetched live from OpenAlex

We performed the non-local thermodynamic equilibrium (non-LTE, NLTE) calculations for Ca i-ii with the updated model atom that includes new quantum-mechanical rate coefficients for Ca i + H i collisions from two recent studies and investigated the accuracy of calcium abundance determinations using the Sun, Procyon, and five metal-poor (MP, −2.6 ≤ [Fe/H] ≤−1.3) stars with well-determined stellar parameters. Including H i collisions substantially reduces over-ionisation of Ca i in the line formation layers compared with the case of pure electronic collisions and thus the NLTE effects on abundances derived from Ca i lines. We show that both collisional recipes lead to very similar NLTE results. As for Ca ii, the classical Drawinian rates scaled by SH = 0.1 are still applied. When using the subordinate lines of Ca i and the high-excitation lines of Ca ii, NLTE provides the smaller line-to-line scatter compared with the LTE case for each star. For Procyon, NLTE removes a steep trend with line strength among strong Ca i lines seen in LTE and leads to consistent [Ca/H] abundances from the two ionisation stages. In the MP stars, the NLTE abundance from Ca ii 8498 Å agrees well with the abundance from the Ca i subordinate lines, in contrast to LTE, where the abundance difference grows towards lower metallicity and reaches 0.46 dex in BD −13°3442 ([Fe/H] = −2.62). NLTE largely removes abundance discrepancies between the high-excitation lines of Ca ii and Ca ii 8498 Å obtained for our four [Fe/H] < −2 stars under the LTE assumption. We investigated the formation of the Ca i resonance line in the [Fe/H] < −2 stars. When the calcium abundance varies between [Ca/H] ≃ −1.8 and −2.3, photon loss in the resonance line itself in the uppermost atmospheric layers drives the strengthening of the line core compared with the LTE case, and this effect prevails over the weakening of the line wings, resulting in negative NLTE abundance correction and underestimation of the abundance derived from Ca i 4226 Å compared with that from the subordinate lines, by 0.08 to 0.32 dex. This problem may be related to the use of classical homogeneous (1D) model atmospheres. The situation is improved when the calcium abundance decreases and the Ca i 4226 Å line formation depths are shifted into deep atmospheric layers that are dominated by over-ionisation of Ca i. However, the departures from LTE are still underestimated for Ca i 4226 Å at [Ca/H] ≃ −4.4 (HE 0557-4840). Consistent NLTE abundances from the Ca i resonance line and the Ca ii lines are found for HE 0107-5240 and HE 1327-2326 with [Ca/H] ≤−5. Thus, the Ca i/Ca ii ionisation equilibrium method can successfully be applied to determine surface gravities of [Ca/H] ≾ −5 stars. We provide the NLTE abundance corrections for 28 lines of Ca i in a grid of model atmospheres with 5000 K ≤ Teff ≤ 6500 K, 2.5 ≤ log g ≤ 4.5, −4 ≤ [Fe/H] ≤ 0, which is suitable for abundance analysis of FGK-type dwarfs and subgiants.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations56
Published2017
Admission routes1
Has abstractyes

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