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Record W3106539027

Precessing flaring magnetar as a source of repeating FRB 180916.J0158+65

2020· article· en· W3106539027 on OpenAlexaboutno aff

Bibliographic record

VenueMPG.PuRe (Max Planck Society) · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersSimons Foundation
KeywordsMagnetarPhysicsAstrophysicsNeutron star
DOInot available

Abstract

fetched live from OpenAlex

Recently, the Canadian Hydrogen Intensity Mapping Experiment detected periodicity in the bursting rate of the repeating FRB 180916.J0158+65. In a popular class of models, the fast radio bursts (FRBs) are created by magnetic flares of a hyperactive magnetar driven by fast ambipolar diffusion in the core. We point out that in this scenario the magnetar is expected to precess freely with a period of weeks to months. The internal magnetic field B ~ 10<sup>16</sup> G deforms the star, and magnetic flares induce sudden changes in magnetic stresses. The resulting torques and displacements of the principal axes of inertia are capable of pumping a significant amplitude of precession. The anisotropy of the flaring FRB activity, combined with precession, implies a strong periodic modulation of the visible bursting rate. The ultrastrong field invoked in the magnetar model provides: (1) energy for the frequent giant flares, (2) the high rate of ambipolar diffusion, releasing the magnetic energy on the timescale ~10<sup>9</sup> s, (3) the core temperature T ≈ 10<sup>9</sup> K, likely above the critical temperature for neutron superfluidity, (4) strong magnetospheric torques, which efficiently spin down the star, and (5) deformation with ellipticity ∊ ≳ 10−<sup>6</sup>, much greater than the rotational deformation. These conditions result in a precession with negligible viscous damping, and can explain the observed 16 day period in FRB 180916.J0158+65. The increase of precession period due to the magnetar spindown should become measurable in the near future.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.295
Teacher spread0.281 · 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 designNot applicable
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

Citations80
Published2020
Admission routes1
Has abstractyes

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