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Record W3006121760 · doi:10.48550/arxiv.2002.04073

Investigating Opacity Modifications and Reaction Rate Uncertainties to Resolve the Cepheid Mass Discrepancy

2020· preprint· en· W3006121760 on OpenAlexaff
Joyce Ann Guzik, Ebraheem Farag, Jakub Ostrowski, Nancy Remage Evans, Hilding R. Neilson, Sofia P. Moschou, J. J. Drake

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCepheid variableOpacityPhysicsAstrophysicsStellar evolutionInstability stripBinary numberEnvelope (radar)Stellar pulsationRadial velocityCosmic distance ladderStarsOpticsGalaxyComputer science

Abstract

fetched live from OpenAlex

Cepheid masses derived from pulsations or binary dynamics are generally lower than those derived from stellar evolution models. Recent efforts have been dedicated to investigating the effects of abundances, mass loss, rotation, convection and overshooting prescriptions for modifying the evolution tracks to reduce or remove this Cepheid mass discrepancy. While these approaches are promising, either alone or in combination, more work is required to distinguish between possible solutions. Here we investigate nuclear reaction rate and opacity modifications on Cepheid evolution using the MESA code. We discuss the effects of opacity increases at envelope temperatures of 200,000-400,000 K proposed to explain the pulsation properties of hybrid main-sequence beta Cep/Slowly Pulsating B (SPB) variables which will evolve into Cepheids. We make use of the RSP nonlinear radial pulsation modeling capability in MESA to calculate periods and radial velocity amplitudes of Galactic Cepheids V1334 Cyg, Polaris, and delta Cep.

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)0.002
metaresearch head score (Gemma)0.016
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
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 science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.202
Teacher spread0.117 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)→Same topicStellar, planetary, and galactic studies→French-language works237,207→