MétaCan
Menu
Back to cohort
Record W2339604878 · doi:10.1103/physrevc.94.054005

Quantum Monte Carlo calculations of two neutrons in finite volume

2016· article· en· W2339604878 on OpenAlexafffund
P. Klos, J. E. Lynn, Ingo Tews, Stefano Gandolfi, Alexandros Gezerlis, H.‐W. Hammer, Martin Hoferichter, A. Schwenk

Bibliographic record

VenuePhysical review. C · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsUniversity of Guelph
FundersNational Science FoundationEuropean Research CouncilLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsExcited stateDiffusion Monte CarloPhysicsQuantum Monte CarloMonte Carlo methodStatistical physicsObservableGround stateNeutronField (mathematics)Ab initioQuantum mechanicsMonte Carlo molecular modelingMathematics

Abstract

fetched live from OpenAlex

Ab initio calculations provide direct access to the properties of pure neutron systems that are challenging to study experimentally. In addition to their importance for fundamental physics, their properties are required as input for effective field theories of the strong interaction. In this work, we perform auxiliary-field diffusion Monte Carlo calculations of the ground state and first excited state of two neutrons in a finite box, considering a simple contact potential as well as chiral effective field theory interactions. We compare the results against exact diagonalizations and present a detailed analysis of the finite-volume effects, whose understanding is crucial for determining observables from the calculated energies. Using the L\"uscher formula, we extract the low-energy $S$-wave scattering parameters from ground- and excited-state energies for different box sizes.

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 score0.009
Threshold uncertainty score0.017

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 studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.028
GPT teacher head0.370
Teacher spread0.342 · 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

Citations24
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePhysical review. CSame topicNuclear physics research studiesFrench-language works237,207