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Cosmology with gravitationally lensed repeating fast radio bursts

2021· article· en· W3096183115 on OpenAlexafffund
O. Wucknitz, L. G. Spitler, Ue‐Li Pen

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoPerimeter InstituteCanadian Institute for Advanced Research
FundersNatural Sciences and Engineering Research Council of CanadaMax-Planck-GesellschaftAlexander von Humboldt-Stiftung
KeywordsPhysicsCosmologyRedshiftGravitationAstrophysicsInterferometryGravitational waveAstronomyGalaxy

Abstract

fetched live from OpenAlex

High-precision cosmological probes have revealed a small but significant tension between the parameters measured with different techniques, among which there is one based on time delays in gravitational lenses. We discuss a new way of using time delays for cosmology, taking advantage of the extreme precision expected for lensed fast radio bursts, which are short flashes of radio emission originating at cosmological distances. With coherent methods, the achievable precision is sufficient for measuring how time delays change over the months and years, which can also be interpreted as differential redshifts between the images. It turns out that uncertainties arising from the unknown mass distribution of gravitational lenses can be eliminated by combining time delays with their time derivatives. Other effects, most importantly relative proper motions, can be measured accurately and disentangled from the cosmological effects. With a mock sample of simulated lenses, we show that it may be possible to attain strong constraints on cosmological parameters. Finally, the lensed images can be used as galactic interferometer to resolve structures and motions of the burst sources with incredibly high resolution and help reveal their physical nature, which is currently unknown.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.716

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 science0.0000.000
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations38
Published2021
Admission routes2
Has abstractyes

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