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Record W4385443274 · doi:10.1093/mnras/stad2318

High-resolution VLBI astrometry of pulsar scintillation screens with the <i>θ - θ</i> transform

2023· article· en· W4385443274 on OpenAlexafffund
Daniel Baker, Walter Brisken, M. H. van Kerkwijk, R. van Lieshout, Ue‐Li Pen

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced ResearchAlexander von Humboldt-StiftungSimons Foundation
KeywordsPhysicsAstrometryPulsarVery-long-baseline interferometryInterferometryOffset (computer science)ScintillationOpticsData reductionPhase (matter)Remote sensingAstrophysicsStars

Abstract

fetched live from OpenAlex

ABSTRACT The recent development of $\theta - \theta$ techniques in pulsar scintillometry has opened the door for new high-resolution imaging techniques of the scattering medium. By solving the phase retrieval problem and recovering the wavefield from a pulsar dynamic spectrum, the Doppler shift, time delay, and phase offset of individual images can be determined. However, the results of phase retrieval from a single dish are only known up to a constant phase rotation, which introduces extra parameters when doing astrometry using very long baseline interferometry. We present an extension to previous $\theta - \theta$ methods using the interferometric visibilities between multiple stations to calibrate the wavefields. When applied to existing data for PSR B0834+06, we measure the effective screen distance and lens orientation with five times greater precision than was possible in previous work.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.248
Teacher spread0.239 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

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