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Record W4401076225 · doi:10.1088/1475-7516/2024/07/069

Accretion onto oscillating cosmic string loops

2024· article· en· W4401076225 on OpenAlexaff
Hao Jiao, Bryce Cyr, Robert Brandenberger

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsAccretion (finance)Cosmic stringRedshiftLoop (graph theory)Structure formationAstrophysicsString (physics)Theoretical physicsGalaxy

Abstract

fetched live from OpenAlex

Abstract Cosmic string loops are non-linear density fluctuations which form in the early universe and could play an important role in explaining many phenomena which are in tension with the standard ΛCDM model. Hence, the accretion process onto cosmic string loops should be studied in detail. Most previous works view loops as point masses and ignore the impact of a finite loop size. In this work, we utilize the Zel'dovich approximation to calculate the non-linear mass sourced by a static extended loop with a time-averaged density profile derived from the trajectory of the loop oscillation, and compare the result with what is obtained for a point-mass source. We find that the finite size of a loop mainly affects the evolution of turnaround shells during the early stages of accretion, converging to the point mass result after a critical redshift, zc ( II)/( III) . For zc ( II)/( III) , the total accreted mass surrounding a loop is suppressed relative to the point mass case and has a growth rate proportional to (1+ z ) -3/2 . As an immediate extension, we also qualitatively analyse the accretion onto moving point masses and onto moving extended loops. In addition to the reduction in the nonlinear mass, the loop finite size also changes the shape of the turnaround surface at early stages of accretion.

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 score0.097
Threshold uncertainty score0.418

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 communication0.0000.001
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 head0.011
GPT teacher head0.238
Teacher spread0.228 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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