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Record W2245723612 · doi:10.1093/mnras/stu1020

Pulsar scintillations from corrugated reconnection sheets in the interstellar medium

2014· article· en· W2245723612 on OpenAlexafffund
Ue‐Li Pen, Y. Levin

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoCanadian Institute for Advanced Research
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaARC Centre of Excellence for All-Sky Astrophysics
KeywordsPhysicsPulsarInterstellar mediumScintillationAstrophysicsAstronomyGalaxyOptics

Abstract

fetched live from OpenAlex

We show that surface waves along interstellar current sheets closely aligned with the line of sight lead to pulsar scintillation properties consistent with those observed. This mechanism naturally produces the length-scales and density scales of the interstellar medium (ISM) scattering lenses that are required to explain the magnitude and dynamical spectrum of the scintillations. In this scenario, the parts of warm ionized ISM that are responsible for the scintillations are relatively quiescent, with scintillation and scattering resulting from weak waves propagating along magnetic domain boundary current sheets. These are expected from helicity conservation and have also been observed in numerical simulations. The model statistically predicts the spacing and amplitudes of inverted parabolic arcs seen in Fourier-transformed dynamical spectra of strongly scintillating pulsars with only three parameters. Multifrequency, multi-epoch low-frequency very long baseline interferometry observations can quantitatively test this. If successful, in addition to mapping the ISM, this might allow precise nanoarcsecond pulsar astrometry, distance measurements and emission studies using these 10-au interferometers in the sky.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.343

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)0.0000.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations92
Published2014
Admission routes2
Has abstractyes

Explore more

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