Discretization of 4D Poincaré BF theory: From groups to 2-groups
Bibliographic record
Abstract
We study the discretization of a Poincar\'e/Euclidean BF theory. Upon the addition of a boundary term this theory is equivalent to the BFCG theory defined in terms of the Poincar\'e/Euclidean 2-group. At an intermediate step in the discretization, we note that there are multiple options for how to proceed. One option brings us back to recovering the discrete variables and phase space of the BF theory. Another option allows us to rediscover the phase space related to the $G$ networks given by Asante et al.. Indeed, our main result is that we are now able to relate the continuum fields with the discrete variables shown by Asante et al. This relation is important in determining how to implement the simplicity constraints to recover gravity using the BFCG action. In fact, we show that such a relation is not as simple as in the BF discretization: the discretized variable on the triangles actually depend on several of the continuum fields instead of solely the continuum $B$ field. We also compare and contrast the discretized BF and BFCG models as pairs of of dual 2-groups. This work highlights (again) how the choice of boundary term influences the resulting symmetry structure of the discretized theory---and hence ultimately the choice of quantum states.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".