Asevolution: a relativistic N-body implementation of the (a)symmetron
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".