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Record W4379474666 · doi:10.48550/arxiv.2306.01617

The Radio Parallax of the Crab Pulsar: A First VLBI Measurement Calibrated with Giant Pulses

2023· preprint· en· W4379474666 on OpenAlexfundno aff
Rebecca Lin, M. H. van Kerkwijk, Franz Kirsten, Ue‐Li Pen, Adam T. Deller

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science and Technology CouncilKillam TrustsUniversity of TorontoCanadian Institute for Advanced ResearchGovernment of OntarioAlexander von Humboldt-StiftungNational Natural Science Foundation of ChinaNational Radio Astronomy ObservatoryVetenskapsrådetNational Science Foundation
KeywordsVery-long-baseline interferometryPhysicsCrab NebulaParallaxAstrometryPulsarCrab PulsarAstrophysicsCalibrationAstronomyProper motionStars

Abstract

fetched live from OpenAlex

We use four observations with the European VLBI network to measure the first precise radio parallax of the Crab Pulsar. We found two in-beam extragalactic sources just outside the Crab Nebula, with one bright enough to use as a background reference source in our data. We use the Crab Pulsar's giant pulses to determine fringe and bandpass calibration solutions, which greatly improved the sensitivity and reliability of our images and allowed us to determine precise positional offsets between the pulsar and the background source. From those offsets, we determine a parallax of $π=0.53\pm0.06\rm{\;mas}$ and proper motion of $(μ_α,μ_δ)=(-11.34\pm0.06,2.65\pm0.14)\rm{\;mas\;yr^{-1}}$, yielding a distance of $d=1.90^{+0.22}_{-0.18}\rm{\;kpc}$ and transverse velocity of $v_{\perp}=104^{+13}_{-11}\rm{\;km\;s^{-1}}$. These results are consistent with the Gaia 3 measurements, and open up the possibility of far more accurate astrometry with further VLBI observations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.166
Teacher spread0.092 · 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 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
Published2023
Admission routes1
Has abstractyes

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