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Record W2753717147 · doi:10.3847/1538-4357/aa8ad6

Pressure Distortion of the H<sub>2</sub>–He Collision-induced Absorption at the Photosphere of Cool White Dwarf Stars

2017· article· en· W2753717147 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOpacityWhite dwarfPhotosphereAbsorption (acoustics)Spectral lineDistortion (music)Absorption spectroscopyMolecular cloudStars

Abstract

fetched live from OpenAlex

Abstract Collision-induced absorption (CIA) from molecular hydrogen is a dominant opacity source in the atmosphere of cool white dwarfs. It results in a significant flux depletion in the near-IR and IR parts of their spectra. Because of the extreme conditions of helium-rich atmospheres (where the density can be as high as a few g cm −3 ), this opacity source is expected to undergo strong pressure distortion and the currently used opacities have not been validated at such extreme conditions. To check the distortion of the CIA opacity, we applied state-of-the-art ab initio methods of computational quantum chemistry to simulate the CIA opacity at high densities. The results show that the CIA profiles are significantly distorted above densities of in a way that is not captured by the existing models. The roto-translational band is enhanced and shifted to higher frequencies as an effect of the decrease of the interatomic separation of the H 2 molecule. The vibrational band is blueward shifted and split into Q R and Q P branches, separated by a pronounced interference dip. Its intensity is also substantially reduced. The distortions result in a shift of the maximum of the absorption from 2.3 μ m to 3–7 μ m, which could potentially explain the spectra of some very cool, helium-rich white dwarfs.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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