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Record W1775073634 · doi:10.1103/physrevb.76.054521

Bipairing and the stripe phase in four-leg Hubbard ladders

2007· article· en· W1775073634 on OpenAlexaff
Ming-Shyang Chang, Ian Affleck

Bibliographic record

VenuePhysical Review B · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBosonizationDensity matrix renormalization groupPhysicsHubbard modelRenormalization groupCondensed matter physicsQuantum mechanicsElectronCoupling (piping)Phase (matter)Mathematical physicsLimit (mathematics)Quantum electrodynamicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Density matrix renormalization group (DMRG) calculations on four-leg $t\text{\ensuremath{-}}J$ and Hubbard ladders have found a phase exhibiting ``stripes'' at intermediate doping. Such behavior can be viewed as generalized Friedel oscillations, with wavelength equal to the inverse hole density, induced by the open boundary conditions. So far, this phase has not been understood using the conventional weak-coupling bosonization approach. Based on studies of a general bosonization proof, finite size spectrum, an improved analysis of weak-coupling renormalization group equations, and the decoupled two-leg ladder limit, we here find new types of phases of four-leg ladders, which exhibit stripes. They also inevitably exhibit ``bipairing,'' meaning that there is a gap to add one or two electrons (but not four) and that both single electron and electron pair correlation functions decay exponentially, while correlation functions of charge-4 operators exhibit a power-law decay. Whether or not bipairing occurs in the stripe phase found in DMRG is an important open question.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.022
GPT teacher head0.326
Teacher spread0.304 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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