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Record W4319337436 · doi:10.1088/1674-4527/acb9de

An Intermediate-field Fast Radio Burst Model and the Quasi-periodic Oscillation

2023· article· en· W4319337436 on OpenAlexafffund
J. S. Wang, Xinyu Li, Zi-Gao Dai, Xue-Feng Wu

Bibliographic record

VenueResearch in Astronomy and Astrophysics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsPerimeter Institute
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueNational Natural Science Foundation of ChinaGovernment of CanadaMinistry of Colleges and UniversitiesAlexander von Humboldt-Stiftung
KeywordsPhysicsMagnetarAstrophysicsMagnetosphereMagnetic fieldOscillation (cell signaling)Fast radio burstMagnetic reconnectionNeutron star

Abstract

fetched live from OpenAlex

Abstract Quasi-periodic oscillation (QPO) signals are discovered in some fast radio bursts (FRBs) such as FRB 20191221A, as well as in the X-ray burst associated with the galactic FRB from SGR 1935+2154. We revisit the intermediate-field FRB model where the radio waves are generated as fast-magnetosonic waves through magnetic reconnection near the light cylinder. The current sheet in the magnetar wind is compressed by a low frequency pulse emitted from the inner magnetosphere to trigger magnetic reconnection. By incorporating the wave dynamics of the magnetosphere, we demonstrate how the FRB frequency, the single pulse width, and luminosity are determined by the period, magnetic field, QPO frequency and quake energetics of the magnetar. We find that this model can naturally and self-consistently interpret the X-ray/radio event from SGR 1935+2154 and the QPO in FRB 20191221A. It can also explain the observed wide energy range of repeating FRBs in a narrow bandwidth.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.030
GPT teacher head0.365
Teacher spread0.335 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2023
Admission routes2
Has abstractyes

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