Searches for Path Length Dependent Jet Quenching in JETSCAPE
Bibliographic record
Abstract
The primary objective of this thesis is to research path length-dependent jet quenching in quark-gluon plasma (QGP) produced during ultra-relativistic heavy ion collisions at the Large Hadron Collider (LHC). This was done using the JETSCAPE framework, that allows for modular simulation of heavy-ion collisions, using the latest Monte Carlo simulators available for each stage of the process. This was done using two sets of modules, the AA hard tune and the PP19 tune. The AA hard tune simulated a heavy-ion collision with a hydrodynamic medium, alongside jets produced by Pythia to probe this medium. While the PP19 tune simulated proton-proton collisions, once again producing jets with Pythia. By investigating the position of the hard scatterings generated in the AA hard tune, under different trigger conditions, path length dependent jet quenching was observed. However, this was a much smaller effect than what was expected. As such two particle correlations to investigate the shapes of the jets as a function of path length was not possible. However, correlation functions for AA hard and PP19 tune were done regardless, as this is the first time something like this has been done in JETSCAPE. Comparing these revealed the effects of QGP in modifying the shape of the jet peaks, as well as correlations from the bulk medium.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".