Jet and electromagnetic tomography (JET) of extreme phases of matter in heavy-ion collisions
Bibliographic record
Abstract
The Ohio State University (OSU) group contributed to the deliverables of the JET Collaboration three major products: 1. The code package iEBE-VISHNU for modeling the dynamical evolution of the soft medium created in relativistic heavy-ion collisions, from its creation all the way to final freeze-out using a hybrid approach that interfaces a free-streaming partonic pre-equilbrium stage with a (2+1)-dimensional viscous relativistic fluid dynamical stage for the quark-gluon plasma (QGP) phase and the microscopic hadron cascade UrQMD for the hadronic rescattering and freeze-out stage. Except for UrQMD, all dynamical evolution components and interfaces were developed at OSU and tested and implemented in collaboration with the Duke University group. 2. An electromagnetic radiation module for the calculation of thermal photon emission from the QGP and hadron resonance gas stages of a heavy-ion collision, with emission rates that have been corrected for viscous effects in the expanding medium consistent with the bulk evolution. The electromagnetic radiation module was developed under OSU leadership in collaboration with the McGill group and has been integrated in the iEBE-VISHNU code package. 3. An interface between the Monte Carlo jet shower evolution and hadronization codes developed by the Wayne State University (WSU), McGill and Texas A&M groups and the iEBE-VISHNU bulk evolution code, for performing jet quenching and jet shape modification studies in a realistically modeled evolving medium that was tuned to measured soft hadron data. Building on work performed at OSU for the theoretical framework used to describe the interaction of jets with the medium, initial work on the jet shower Monte Carlo was started at OSU and moved to WSU when OSU Visiting Assistant Professor Abhijit Majumder accepted a tenure track faculty position at WSU in September 2011. The jet-hydro interface was developed at OSU and WSU and tested and implemented in collaboration with the McGill, Texas A&M, and LBNL groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".