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Record W4387821400 · doi:10.1002/lpor.202300448

Spectroscopic Signatures of Plasmonic Near‐Fields on High‐Harmonic Emission

2023· article· en· W4387821400 on OpenAlexafffund
Sohail A. Jalil, Kashif M. Awan, Joshua Baxter, Graeme Bart, David N. Purschke, Thomas Fennel, D. M. Villeneuve, A. Staudte, Pierre Berini, Thomas Brabec, Lora Ramunno, Giulio Vampa

Bibliographic record

VenueLaser & Photonics Review · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsCarleton UniversityJoint Attosecond Science LaboratoryUniversity of Ottawa
FundersStewart Blusson Quantum Matter Institute, University of British ColumbiaDeutsche Forschungsgemeinschaft
KeywordsAttosecondPlasmonFemtosecondHarmonicsHigh harmonic generationPhysicsLaserPhotonElectronPhotoelectric effectOpticsOptoelectronicsUltrashort pulseQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Intense laser fields can reveal the attosecond and femtosecond response of matter in the emitted photoelectrons and high‐harmonic photons. The complementary perspective offered by these two messengers is well explored in gas molecules and, more recently, in bulk solids, where both electron emission and high‐order harmonics have been utilized to probe the laser‐matter interaction. In nanoscale solids, electron emission provides a wealth of information about the localized and inhomogeneous near fields around the nanoparticles. Here, it is shown experimentally that inhomogeneous fields also affect high‐order harmonics. Specifically, the experiment reveals strong indications that the field gradient of a nanoscale plasmonic hotspot found inside a Si crystal induces the emission of even‐order high harmonics from the crystal itself. This demonstration extends the complementary electron‐photon perspective on attosecond science to nanoscale systems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.001

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 head0.014
GPT teacher head0.299
Teacher spread0.285 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2023
Admission routes2
Has abstractyes

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