Applications of many-body physics to relativistic heavy ion collisions
Bibliographic record
Abstract
In this dissertation, many-body physics techniques are used to study and improve ideas related to the description of heavy ion collisions at very high energy. The first part of the thesis concerns the production of tensor mesons in proton-proton (pp) collisions. An effective theory where the f2 meson couples to the energy-momentum tensor is proposed and a comparison of the inclusive cross-section computed in the collinear factorization, the kT-factorization and the color glass condensate is performed. A study of the phenomenology in pp collisions then shows a strong dependence on the parametrization of the unintegrated distribution function. The conclusion is that f2 meson production can be utilized to improve the understanding of the proton wave-function. In the second part, a similar investigation is performed by analysing the production cross-section of the eta' meson in pp and proton-nucleus (pA) collisions. The nucleus and proton are described by the CGC and the kT-factorization respectively. A new technique for the computation of Wilson lines - color charge densities correlators in the McLerran-Venugopalan model is developped. The phenomenology shows that the cross-section in pA collisions is very sensitive to the value of the saturation scale, a crucial ingredient of the CGC picture. In the third part of the thesis, the collision term of the Boltzmann equation is derived from first principles at all orders and for any number of participating particles, starting from the full out-of-equilibrium quantum field theory and using the multiple scattering expansion. Finally, the emission of photons from a non-abelian strong classical field is investigated. A formalism based on Schwinger-Keldysh propagators relating the production rate of photons to the retarded solution of the Dirac equation in a background field is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".