Multipoint lightcone bootstrap from differential equations
Bibliographic record
Abstract
Abstract One of the most striking successes of the lightcone bootstrap has been the perturbative computation of the anomalous dimensions and OPE coefficients of double-twist operators with large spin. It is expected that similar results for multiple-twist families can be obtained by extending the lightcone bootstrap to multipoint correlators. However, very little was known about multipoint lightcone blocks until now, in particular for OPE channels of comb topology. Here, we develop a systematic theory of lightcone blocks for arbitrary OPE channels based on the analysis of Casimir and vertex differential equations. Most of the novel technology is developed in the context of five- and six-point functions. Equipped with new expressions for lightcone blocks, we analyze crossing symmetry equations and compute OPE coefficients involving two double-twist operators that were not known before. In particular, for the first time, we are able to resolve a discrete dependence on tensor structures at large spin. The computation of anomalous dimensions for triple-twist families from six-point crossing equations will be addressed in a sequel to this work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".