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Record W4225417048 · doi:10.1007/jhep05(2022)004

Friction pressure on relativistic bubble walls

2022· article· en· W4225417048 on OpenAlexfundno aff
Yann Gouttenoire, Ryusuke Jinno, Filippo Sala

Bibliographic record

VenueJournal of High Energy Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceAzrieli FoundationDeutsche Forschungsgemeinschaft
KeywordsPhysicsBubbleBosonCoupling (piping)Phase transitionLorentz transformationOrder (exchange)Monte Carlo methodPlasmaQuantum electrodynamicsClassical mechanicsMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

A bstract During a cosmological first-order phase transition, particles of the plasma crossing the bubble walls can radiate a gauge boson. The resulting pressure cannot be computed perturbatively for large coupling constant and/or large supercooling. We resum the real and virtual emissions at all leading-log orders, both analytically and numerically using a Monte-Carlo simulation. We find that radiated bosons are dominantly soft and that the resulting retarding pressure on relativistic bubble walls is linear both in the Lorentz boost and in the order parameter, up to a log. We further quantitatively discuss IR cut-offs, wall thickness effects, the impact of various approximations entering the calculation, and comment on the fate of radiated bosons that are reflected.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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 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

Citations124
Published2022
Admission routes1
Has abstractyes

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