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Record W2767342671 · doi:10.1051/epjconf/201817505023

More on heavy tetraquarks in lattice QCD at almost physical pion mass

2018· article· en· W2767342671 on OpenAlexafffund

Bibliographic record

VenueEPJ Web of Conferences · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum Chromodynamics and Particle Interactions
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsTetraquarkQuarkCharm quarkPionCharm (quantum number)Lattice QCDOperator (biology)Quantum chromodynamics

Abstract

fetched live from OpenAlex

We report on our progress in studying exotic, heavy tetraquark states, qq′ Q̅Q̅′. Using publicly available dynamicalnf= 2 + 1 Wilson-Clover gauge configurations, generated by the PACS-CS collaboration, with pion masses ≃ 164, 299 and 415 MeV, we extend our previous analysis to heavy quark components containing heavier than physical bottom quarks Q̅Q̅′ =b̅′b̅′or Q̅Q̅′ =b̅b̅′, charm and bottom quarksc̅b̅and also only charm quarks c̅ c̅. Throughout we employ NRQCD and relativistic heavy quarks for the heavier than bottom, bottom and charm quarks. Using our previously established diquark-antidiquark and meson-meson operator basis we comment in particular on the dependence of the binding energy on the mass of the heavy quark componentQ̅Q̅, with heavy quarks ranging frommQ= 0:85… 6.3 ·mb. In the heavy flavor non-degenerate case,Q̅Q̅′, and especially for the tetraquark channeludc̅d̅, we extend our work to utilize a 3 × 3 GEVP to study the ground and threshold states thereby enabling a clear identification of possible binding. Finally, we present initial work on theQ̅Q̅′=c̅c̅system where a much larger operator basis is available in comparison to flavor combinations with NRQCD quarks.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.018
GPT teacher head0.302
Teacher spread0.284 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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