Aspects of Resummation in Effective and Finite Temperature Field Theory
Bibliographic record
Abstract
This thesis focuses on developing resummation techniques to study collider observables and cosmological phase transitions. In the first part of the thesis, an alternative formalism of Soft-Collinear Effective Theory (SCET) is used to study next-to-leading power (NLP) corrections in deep inelastic scattering (DIS) in the endpoint limit ($x \rightarrow 1$) and Drell-Yan production of gauge boson with transverse momentum small compared to their invariant mass (small-$q_T$). In the second part of the thesis, a modified version of the Optimized Partial Dressing (OPD) scheme is developed for perturbative thermal resummation of effective potential at finite temperature. The formulation of SCET used here does not separate the infrared degrees of freedom into separate modes, which makes it calculationally simpler to study NLP corrections. This is used to obtain the inclusive cross-section for DIS in the endpoint limit. It is also demonstrated that spurious endpoint divergence which appear at NLP is cancelled by the overlap subtraction which is required to remove double counting at leading power. For Drell-Yan production at small transverse momentum this formalism is used to calculate the inclusive cross-section in terms of power-suppressed operators which are also renormalized in the rapidity space. Finally for the work related to thermal resummation, it is shown that the original formulation of OPD scheme has large scale dependence when compared to techniques like Dimensional Reduction. It is demonstrated analytically and numerically that this scale dependence is significantly reduced for a $\phi^4$ theory by the inclusion of two loop sunset graphs and its daisy descendants.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".