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Record W6910651208 · doi:10.48550/arxiv.2005.10324

A bright millisecond-duration radio burst from a Galactic magnetar

2020· preprint· en· W6910651208 on OpenAlexaboutno aff

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetarNeutron starGalaxyFast radio burstFlarePopulationMilky Way

Abstract

fetched live from OpenAlex

Magnetars are highly magnetized young neutron stars that occasionally produce enormous bursts and flares of X-rays and gamma-rays. Of the approximately thirty magnetars currently known in our Galaxy and Magellanic Clouds, five have exhibited transient radio pulsations. Fast radio bursts (FRBs) are millisecond-duration bursts of radio waves arriving from cosmological distances. Some have been seen to repeat. A leading model for repeating FRBs is that they are extragalactic magnetars, powered by their intense magnetic fields. However, a challenge to this model has been that FRBs must have radio luminosities many orders of magnitude larger than those seen from known Galactic magnetars. Here we report the detection of an extremely intense radio burst from the Galactic magnetar SGR 1935+2154 using the Canadian Hydrogen Intensity Mapping Experiment (CHIME) FRB project. The fluence of this two-component bright radio burst and the estimated distance to SGR 1935+2154 together imply a 400-800 MHz burst energy of $\sim 3 \times 10^{34}$ erg, which is three orders of magnitude brighter than those of any radio-emitting magnetar detected thus far. Such a burst coming from a nearby galaxy would be indistinguishable from a typical FRB. This event thus bridges a large fraction of the radio energy gap between the population of Galactic magnetars and FRBs, strongly supporting the notion that magnetars are the origin of at least some FRBs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.051
GPT teacher head0.223
Teacher spread0.172 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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