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Record W2886514254 · doi:10.1051/epjconf/201818202044

Exploring finite-size effects in strongly correlated systems

2018· article· en· W2886514254 on OpenAlexafffund
Mateusz Buraczynski, William G. Dawkins, Nawar Ismail, Alexandros Gezerlis

Bibliographic record

VenueEPJ Web of Conferences · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceNational Energy Research Scientific Computing Center
KeywordsThermodynamic limitStatistical physicsPhysicsLimit (mathematics)Quantum Monte CarloAb initioMonte Carlo methodGround stateWork (physics)QuantumTheoretical physicsQuantum mechanicsMathematicsStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

Complexities greatly limit any study of strongly correlated systems to a small number of particles. Thus, any attempt at understanding infinite systems such as those arising from neutron matter (NM) must consider finite-size (FS) effects at play when below the thermodynamic limit (TL). In these conference proceedings we provide some examples of FS effects at work and discuss our prescription for extrapolating the physics of extended systems. We present our methodology and calculations performed for an assortment of strongly correlated (SC) systems. Ab initio, non-perturbative Quantum Monte Carlo (QMC) methods can be employed to accurately compute ground-state energies and finite-temperature properties. We apply these to periodically modulated NM and use our results to constrain phenomenological theories of nuclei and study the static response of NM.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.462

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)0.0000.000

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 head0.064
GPT teacher head0.289
Teacher spread0.225 · 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 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

Citations1
Published2018
Admission routes2
Has abstractyes

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