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Record W2257253185 · doi:10.1093/mnras/stv2112

New expansion rate measurements of the Crab nebula in radio and optical

2015· article· en· W2257253185 on OpenAlexaff
M. F. Bietenholz, R. L. Nugent

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsYork University
Fundersnot available
KeywordsPhysicsCrab NebulaNebulaAstrophysicsSynchrotronPulsarAstronomyPulsar wind nebulaSupernovaEmission nebulaSupernova remnantSynchrotron radiationProtoplanetary nebulaStarsOptics

Abstract

fetched live from OpenAlex

We present new radio measurements of the expansion rate of the Crab nebula's synchrotron nebula over a ∼30-yr period. We find a convergence date for the radio synchrotron nebula of ce 1255 ± 27. We also re-evaluated the expansion rate of the optical-line-emitting filaments, and we show that the traditional estimates of their convergence date are slightly biased. Using an unbiased Bayesian analysis, we find a convergence date for the filaments of ce 1091 ± 34 (∼40 yr earlier than previous estimates). Our results show that both the synchrotron nebula and the optical-line-emitting filaments have been accelerated since the explosion in ce 1054, but that the synchrotron nebula has been relatively strongly accelerated, while the optical filaments have been only slightly accelerated. The finding that the synchrotron emission expands more rapidly than the filaments supports the picture that the latter are the result of the Rayleigh–Taylor instability at the interface between the pulsar-wind nebula and the surrounding freely expanding supernova ejecta, and rules out models where the pulsar-wind bubble is interacting directly with the pre-supernova wind of the Crab's progenitor.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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