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Record W4367693061 · doi:10.1088/1475-7516/2023/05/009

Asevolution: a relativistic N-body implementation of the (a)symmetron

2023· article· en· W4367693061 on OpenAlexaff
Øyvind Christiansen, Farbod Hassani, Mona Jalilvand, David F. Mota

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsScalar fieldSolverClassical mechanicsField (mathematics)Theoretical physicsScalar (mathematics)Metric (unit)UniverseStatistical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We present asevolution, a cosmological N -body code developed based on gevolution, which consistently solves for the (a)symmetron scalar field and metric potentials within the weak-field approximation. In asevolution, the scalar field is dynamic and can form non-linear structures. A cubic term is added in the symmetron potential to make the symmetry-broken vacuum expectation values different, which is motivated by observational tensions in the late-time universe. To study the effects of the scalar field dynamics, we also implement a constraint solver making use of the quasi-static approximation, and provide options for evaluating the background evolution, including using the full energy density averaged over the simulation box within the Friedmann equation. The asevolution code is validated by comparison with the Newtonian N -body code ISIS that makes use of the quasi-static approximation. There is found a very small effect of including relativistic and weak-field corrections in our small test simulations; it is seen that for small masses, the field is dynamic and can not be accurately solved for using the quasi-static approximation; and we observe the formation of unstable domain walls and demonstrate a useful way to identify them within the code. A first consideration indicates that the domain walls are more unstable in the asymmetron scenario.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.182

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 communication0.0000.000
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 head0.009
GPT teacher head0.286
Teacher spread0.276 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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