BubbleProfiler: finding the field profile and action for cosmological phase transitions
Bibliographic record
Abstract
We present BubbleProfiler, a C++ software package for finding field profiles in bubble walls and calculating the bounce action during phase transitions involving multiple scalar fields. Our code uses a recently proposed perturbative method for potentials with multiple fields and a shooting method for single field cases. BubbleProfiler is constructed with modularity, flexibility and practicality in mind. These principles extend from the input of an arbitrary potential with multiple scalar fields in various forms, through the code structure, to the testing suite. After reviewing the physics context, we describe how the methods are implemented in BubbleProfiler, provide an overview of the code structure and detail usage scenarios. We present a number of examples that serve as test cases of BubbleProfiler and comparisons to existing public codes with similar functionality. We also show a physics application of BubbleProfiler in the scalar singlet extension of the Standard Model of particle physics by calculating the action as a function of model parameters during the electroweak phase transition. BubbleProfiler completes an important link in the toolchain for studying the properties of the thermal phase transition driving baryogenesis and properties of gravitational waves in models with multiple scalar fields. The code can be obtained from: https://github.com/bubbleprofiler/bubbleprofiler
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".