Larger twists and higher n-point functions with fractional conformal descendants in SN orbifold CFTs at large N
Bibliographic record
Abstract
Abstract We consider correlation functions in symmetric product (SN) orbifold CFTs at large N with arbitrary seed CFT, expanding on our earlier work [1]. Using covering space techniques, we calculate descent relations using fractional Virasoro generators in correlators, writing correlators of descendants in terms of correlators of ancestors. We first consider the case three-point functions of the form (m-cycle)-(n-cycle)-(q-cycle) which lift to arbitrary primaries on the cover, and descendants thereof. In these examples we show that the correlator descent relations make sense in the base space orbifold CFT, but do not depend on the specific details of the seed CFT. This makes these descent relations universal in all SN orbifold CFTs. Next, we explore four-point functions of the form (2-cycle)-(n-cycle)-(n-cycle)-(2-cycle) which lift to arbitrary primaries on the cover, and descendants thereof. In such cases a single parameter in the map s parameterizes both the base space cross ratio ζz and the covering space cross ratio ζt. We find that the correlator descent relations for the four point function make sense in the base space orbifold CFT as well, arguing that the dependence on the parameter s is tantamount to writing the descent relations in terms of the base space cross ratio. These descent relations again do not depend on the specifics of the seed CFT, making these universal as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".