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Record W3048209480 · doi:10.3847/1538-4357/abad32

13 yr of P Cygni Spectropolarimetry: Investigating Mass Loss through Hα, Periodicity, and Ellipticity

2020· article· en· W3048209480 on OpenAlexaff
Keyan Gootkin, Trevor Z. Dorn-Wallenstein, Jamie R. Lomax, Gwendolyn M. Eadie, Emily M. Levesque, B. Babler, Jennifer L. Hoffman, M. R. Meade, K. H. Nordsieck, John P. Wisniewski

Bibliographic record

VenueThe Astrophysical Journal · 2020
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAstrophysicsPhysicsAstronomy

Abstract

fetched live from OpenAlex

Abstract We report on over 13 yr of optical and near-ultraviolet spectropolarimetric observations of the famous luminous blue variable (LBV) P Cygni. In the lives of the most massive stars, LBVs are a critical transitional phase and achieve the largest mass-loss rates of any group of stars. Using spectropolarimetry, we are able to learn about the geometry of the near-circumstellar environment surrounding P Cygni and gain insights into LBV mass loss. Using data from the HPOL and WUPPE spectropolarimeters, we estimate the interstellar polarization contribution to P Cygni’s spectropolarimetric signal, analyze the variability of the polarization across the Hα emission line, search for periodic signals in the data, and introduce a statistical method to search for preferred position angles in deviations from spherical symmetry that is novel to astronomy. Our data are consistent with previous findings, showing free electron scattering off of clumps uniformly distributed around the star. This is complicated, however, by structure in the percent polarization of the Hα line and a series of previously undetected periodicities.

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)0.000
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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