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Record W2489084058 · doi:10.3847/2041-8205/829/1/l21

A MAGNETAR-LIKE OUTBURST FROM A HIGH-B RADIO PULSAR

2016· article· en· W2489084058 on OpenAlexaff
R. F. Archibald, V. M. Kaspi, Shriharsh P. Tendulkar, Paul Scholz

Bibliographic record

VenueThe Astrophysical Journal Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMagnetarPulsarPhysicsAstrophysicsFermi Gamma-ray Space TelescopeAstronomyLuminosityTelescopeX-ray pulsarGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT Radio pulsars are believed to have their emission powered by the loss of rotational kinetic energy. By contrast, magnetars show intense X-ray and γ -ray radiation whose luminosity greatly exceeds that due to spin down and magnetar luminosity is believed to be powered by intense internal magnetic fields. A basic prediction of this picture is that radio pulsars of high magnetic field should show magnetar-like emission. Here we report on a magnetar-like X-ray outburst from the radio pulsar PSR J1119–6127, heralded by two short bright X-ray bursts on 2016 July 27 and 28. Using target of opportunity data from the Swift X-ray Telescope and NuSTAR , we show that this pulsar’s flux has brightened by a factor of in the 0.5–10 keV band, and that its previously soft X-ray spectrum has undergone a strong hardening with strong pulsations appearing for the first time above 2.5 keV, with phase-averaged emission detectable up to 25 keV. By comparing Swift -XRT and NuSTAR timing data with a pre-outburst ephemeris derived from Fermi Large Area Telescope data, we find that the source has contemporaneously undergone a large spin-up glitch of amplitude . The collection of phenomena observed thus far in this outburst strongly mirrors those in most magnetar outbursts and provides an unambiguous connection between the radio pulsar and magnetar populations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.817

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 designBench or experimental
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

Citations103
Published2016
Admission routes1
Has abstractyes

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