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Record W4210641193 · doi:10.1051/epjconf/202225904003

From lattice QCD to in-medium heavy-quark interactions via deep learning

2022· article· en· W4210641193 on OpenAlexafffund
Shuzhe Shi, Kai Zhou, Jiaxing Zhao, Swagato Mukherjee, Pengfei Zhuang

Bibliographic record

VenueEPJ Web of Conferences · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsMcGill University
FundersAdvanced Scientific Computing ResearchFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceNuclear PhysicsBundesministerium für Bildung und ForschungNational Natural Science Foundation of ChinaSamsonNvidiaU.S. Department of Energy
KeywordsPhysicsQuantum chromodynamicsQuark–gluon plasmaLattice QCDParticle physicsQuarkLattice (music)ThermalPlasmaNuclear physicsThermodynamics

Abstract

fetched live from OpenAlex

Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks, which can be characterized by real ( V R ( T , r )) and imaginary ( V I ( T , r )) potentials, as functions of temperature and spatial separation. Recently, the masses and thermal widths of up to 3 S and 2 P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network (DNN), here, we obtain model-independent results for V R ( T , r ) and V I ( T , r ). The temperature dependence of V R ( T , r ) was found to be very mild between T ≈ 0 − 330 MeV. Meanwhile, V I ( T , r ) shows rapid increase with T and r , which is much larger than the perturbation theory based expectations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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.

Study designObservational
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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

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