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Record W2417634788 · doi:10.1103/physrevd.95.084049

Testing gravity with pulsar scintillation measurements

2017· article· en· W2417634788 on OpenAlexafffund
Huan Yang, A. Nishizawa, Ue‐Li Pen

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsCanadian Institute for Advanced ResearchUniversity of TorontoPerimeter InstituteUniversity of Waterloo
FundersH2020 Marie Skłodowska-Curie ActionsInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueHorizon 2020 Framework ProgrammeOntario Ministry of Research, Innovation and ScienceNational Science Foundation
KeywordsPulsarScintillationPhysicsAstronomyAstrophysicsOpticsDetector

Abstract

fetched live from OpenAlex

We propose to use pulsar scintillation measurements to test predictions of alternative theories of gravity. Compared to single-path pulsar timing measurements, the scintillation measurements can achieve an accuracy of one part in a thousand within one wave period, which means picosecond scale resolution in time, due to the effect of multipath interference. Previous scintillation measurements of PSR $\mathrm{B}0834+06$ have hours of data acquisition, making this approach sensitive to mHz gravitational waves. Therefore it has unique advantages in measuring the effect of gravity or other mechanisms on light propagation. We illustrate its application in constraining the scalar gravitational-wave background, in which case the sensitivities can be greatly improved with respect to previous limits. We expect much broader applications in testing gravity with existing and future pulsar scintillation 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 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)0.005
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.001
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 head0.059
GPT teacher head0.500
Teacher spread0.441 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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