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Record W4379469575 · doi:10.1021/acsphotonics.2c01925

One-Dimensional “Ghost Imaging” in Electron Microscopy of Inelastically Scattered Electrons

2023· article· en· W4379469575 on OpenAlexafffund
Enzo Rotunno, Simone Gargiulo, Giovanni Maria Vanacore, Chen Mechel, Amir H. Tavabi, Rafal E. Dunin‐Borkowski, Fabrizio Carbone, Ivan Madan, Stefano Frabboni, Tuğrul Güner, Ebrahim Karimi, Ido Kaminer, Vincenzo Grillo

Bibliographic record

VenueACS Photonics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
FundersH2020 Future and Emerging TechnologiesMinistry of Colleges and UniversitiesCanada Research ChairsEuropean Commission
KeywordsElectronPhysicsPhotonElectron microscopeResolution (logic)Quantum entanglementMicroscopyOpticsElectron tomographyQuantum imagingQuantumScanning transmission electron microscopyQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Entanglement and correlation are at the basis of quantum mechanics and have been used in optics to create a framework for “ghost imaging”. We propose that a similar scheme can be used in an electron microscope to exploit the correlation of electrons with the coincident detection of collective mode excitations in a sample. In this way, an image of the sample can be formed on an electron camera even if electrons never illuminated the region of interest directly. This concept, which can be regarded as the inverse of photon-induced near-field electron microscopy, can be used to probe delicate molecules with a resolution that is beyond the wavelength of the collective mode.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.261
Teacher spread0.254 · 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 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

Citations6
Published2023
Admission routes2
Has abstractyes

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