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Record W2768432612 · doi:10.1103/physrevc.97.034625

Comparison of heavy-ion transport simulations: Collision integral in a box

2018· article· en· W2768432612 on OpenAlexaff
Ying-Xun Zhang, Yongjia Wang, M. Colonna, Paweł Danielewicz, Akira Ono, M. B. Tsang, H.H. Wolter, Jun Xu, Lie-Wen Chen, Dan Cozma, Zhao-Qing Feng, Subal Das Gupta, Natsumi Ikeno, Che Ming Ko, Bao-An Li, Qingfeng Li, Zhu-Xia Li, S. Mallik, Yasushi Nara, Tatsuhiko Ogawa, Akira Ohnishi, Dmytro Oliinychenko, Massimo Papa, Hannah Petersen, Jun Su, Taesoo Song, Janus Weil, Ning Wang, Feng-Shou Zhang, Zhen Zhang

Bibliographic record

VenuePhysical review. C · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsMcGill University
FundersNuclear PhysicsNational Key Research and Development Program of ChinaHorizon 2020Natural Science Foundation of Zhejiang ProvinceJapan Society for the Promotion of ScienceOffice of ScienceChina Institute of Atomic EnergyState Administration of Foreign Experts AffairsChinese Academy of SciencesNational Natural Science Foundation of ChinaHelmholtz International Center for FAIRWelch FoundationDeutsche ForschungsgemeinschaftU.S. Department of EnergyNational Science Foundation
KeywordsPauli exclusion principleCollisionStatistical physicsPhysicsPhase spaceFock spaceFermi–Dirac statisticsBlocking (statistics)Fermi Gamma-ray Space TelescopeCode (set theory)CascadeBoltzmann constantQuantum mechanicsComputer scienceElectronMathematicsChemistryStatistics

Abstract

fetched live from OpenAlex

Simulations by transport codes are indispensable to extract valuable physical information from heavy-ion collisions. In order to understand the origins of discrepancies among different widely used transport codes, we compare 15 such codes under controlled conditions of a system confined to a box with periodic boundary, initialized with Fermi-Dirac distributions at saturation density and temperatures of either 0 or 5 MeV. In such calculations, one is able to check separately the different ingredients of a transport code. In this second publication of the code evaluation project, we only consider the two-body collision term; i.e., we perform cascade calculations. When the Pauli blocking is artificially suppressed, the collision rates are found to be consistent for most codes (to within $1%$ or better) with analytical results, or completely controlled results of a basic cascade code. In orderto reach that goal, it was necessary to eliminate correlations within the same pair of colliding particles that can be present depending on the adopted collision prescription. In calculations with active Pauli blocking, the blocking probability was found to deviate from the expected reference values. The reason is found in substantial phase-space fluctuations and smearing tied to numerical algorithms and model assumptions in the representation of phase space. This results in the reduction of the blocking probability in most transport codes, so that the simulated system gradually evolves away from the Fermi-Dirac toward a Boltzmann distribution. Since the numerical fluctuations are weaker in the Boltzmann-Uehling-Uhlenbeck codes, the Fermi-Dirac statistics is maintained there for a longer time than in the quantum molecular dynamics codes. As a result of this investigation, we are able to make judgements about the most effective strategies in transport simulations for determining the collision probabilities and the Pauli blocking. Investigation in a similar vein of other ingredients in transport calculations, like the mean-field propagation or the production of nucleon resonances and mesons, will be discussed in the future publications.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.444
Teacher spread0.396 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations140
Published2018
Admission routes1
Has abstractyes

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