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Record W7162034678 · doi:10.82308/17719

Comparative studies of jet quenching in relativistic heavy ion collisions

2023· dissertation· en· W7162034678 on OpenAlexaboutno aff
Rouzbeh Modarresi-Yazdi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
Fundersnot available
KeywordsJet quenchingParton showerJet (fluid)ColliderLarge Hadron ColliderPlasmaEnergy (signal processing)Parton

Abstract

fetched live from OpenAlex

Heavy ion collisions (HIC) performed at major experimental facilities such as the Large Hadron Collider (LHC, Switzerland) or the Relativistic Heavy Ion Collider (RHIC, USA) produce a novel state of matter known as the Quark-Gluon Plasma (QGP). An important signal of the creation of the QGP is the observation of jet energy loss or jet quenching in these collisions. This thesis studies jet energy loss via two parallel and independent comparative analyses. The first study uses MARTINI in the first, single-stage energy loss simulation to analyze the effect of changing the collision kernel which encodes information about the interactions of the jet with the QGP medium and is a crucial ingredient of energy loss rates. Recent efforts have resulted in this kernel's next-to-leading order (NLO) and non-perturbative (NP) evaluations. New inelastic rates are generated with the higher-order kernels and used in MARTINI simulations of energy loss. The simulations are then compared against those using the leading-order (LO) kernel. Systematic differences are shown between the three rate-sets, which can be absorbed into a re-scaled strong coupling constant within a single-stage energy loss model. Simulation results also demonstrate the need to go beyond a single-stage simulation and the physical necessity of a delayed parton shower or a multi-stage simulation.The second study concerns the first comparative analysis of two important models of jet-medium interactions, MARTINI and CUJET, which employ the AMY-McGill and the DGLV energy loss frameworks, respectively. Two multi-stage models are constructed using JETSCAPE, incorporating MARTINI and CUJET as components in their workflow. The models are then applied to multiple HIC systems to calculate nuclear modification factors of charged hadrons, jets and jet substructure observables such as jet fragmentation function ratio. The simulations also contain photons via jet-medium interactions for the first time. We show systematic differences between the models in jet and jet-medium photon observables. The relation of the observed differences to the inelastic rates of the models is shown and points of improvement are discussed

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.420
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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