Conformal four-point correlators of the 3D Ising transition via the quantum fuzzy sphere
Bibliographic record
Abstract
In conformal field theory (CFT), the four-point correlator is a fundamental object that encodes CFT properties, constrains CFT structures, and connects to the gravitational scattering amplitude in holography theory. However, the four-point correlator of CFTs in dimensions higher than 2D remains largely unexplored due to the lack of non-perturbative tools. In this paper, we introduce a new approach for directly computing four-point correlators of 3D CFTs. Our method employs the recently proposed fuzzy (non-commutative) sphere regularization, and we apply it to the paradigmatic 3D Ising CFT. Specifically, we have computed three different four-point correlators: $\langle σσσσ\rangle$, $\langle σσεε\rangle$, and $\langle σσT_{μν} T_{ρη}\rangle$. Additionally, we verify the crossing symmetry of $\langle σσσσ\rangle$, which is a notable property arising from conformal symmetry. Remarkably, the computed four-point correlators exhibit continuous crossing ratios, showcasing the continuum nature of the fuzzy sphere regularization scheme. This characteristic renders them highly suitable for future theoretical applications, enabling further advancements and insights in 3D CFT.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".