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Quantifying Many-Body Effects by High-Resolution Fourier Transform Scanning Tunneling Spectroscopy

2013· article· en· W2029072312 on OpenAlexaff
S. Grothe, Steven Johnston, Shun Chi, P. Dosanjh, Sarah A. Burke

Bibliographic record

VenuePhysical Review Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQuasiparticleElectronScanning tunneling spectroscopyPhysicsSpectroscopyScatteringAtomic physicsAngle-resolved photoemission spectroscopyPhotoemission spectroscopySurface phononElectron energy loss spectroscopyMaterials scienceQuantum tunnellingCondensed matter physicsPhononElectronic structureOpticsNuclear magnetic resonanceX-ray photoelectron spectroscopyQuantum mechanics

Abstract

fetched live from OpenAlex

High-resolution Fourier transform scanning tunneling spectroscopy (FT-STS) is used to study many-body effects on the surface state of Ag(111). Our results reveal a kink in the otherwise parabolic band dispersion of the surface electrons and an increase in the quasiparticle lifetime near the Fermi energy Ef. The experimental data are accurately modeled with the T-matrix formalism for scattering from a single impurity, assuming that the surface electrons are dressed by the electron-electron and electron-phonon interactions. We confirm the latter as the interaction responsible for the deviations from bare dispersion. We further show how FT-STS can be used to simultaneously extract real and imaginary parts of the self-energy for both occupied and unoccupied states with a momentum and energy resolution competitive with angle-resolved photoemission spectroscopy. From our quantitative analysis of the data we extract a Debye energy of ℏΩD=14±1 meV and an electron-phonon coupling strength of λ=0.13±0.02, consistent with previous results. This proof-of-principle measurement advances FT-STS as a method for probing many body effects, which give rise to a rich array of material properties.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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 designBench or experimental
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

Citations29
Published2013
Admission routes1
Has abstractyes

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