Bibliographic record
Abstract
In this thesis we study jet production at large energies from leptonic collisions. We use the framework of effective theories of Quantum Chromodynamics (QCD) to examine the properties of jets and systematically improve calculations.We first develop a new formulation of soft-collinear effective theory (SCET), the appropriate effective theory for jets. In this formulation, soft and collinear degrees of freedom are described using QCD fields that interact with each other through light-like Wilson lines in external currents. This formulation gives a more intuitive picture of jet processes than the traditional formulation of SCET. In particular, we show how the decoupling of soft and collinear degrees of freedom that occurs at leading order in power counting is explicit to next-to-leading order and likely beyond. We then use this formulation to write the thrust rate in a factorized form at next-to-leading order in the thrust parameter. The rate involves an incomplete sum over final states due to phase space cuts that is enforced by a measurement operator. Subleading corrections require matching onto not only the next-to-next-to leading order SCET operators, but also matching onto subleading measurement operators. We derive the appropriate hard, jet, and soft functions and show they reproduce the expected subleading thrust rate.Next, we renormalize the next-to-leading order dijet operators used for the subleading thrust rate. Constraints on matching coefficients from current conservation and reparametrization invariance are shown. We also discuss the subtleties involved in regulating the infrared divergences of the individual loop diagrams in order to extract the ultraviolet divergences. The results can be used to increase the theoretical precision of the thrust rate.Finally, we study the (exclusive) k_perp and C/A jet algorithms in SCET. Regularizing the virtualites and rapidities of the individual graphs, we are able to write the order(\alpha_s) dijet cross section as the product of separate hard, jet, and soft contributions. We show how to reproduce the Sudakov form factor to next-to-leading logarithmic accuracy previously calculated by the coherent branching formalism. Our result only depends on the running of the hard function, and we comment that regularizing rapidities is not necessary in this case.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".