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Record W3085487750 · doi:10.1117/1.ap.2.5.056002

High-spatial-resolution ultrafast framing imaging at 15 trillion frames per second by optical parametric amplification

2020· article· en· W3085487750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Photonics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersShenzhen Fundamental Research ProgramNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsUltrashort pulseFemtosecondImage resolutionOpticsTemporal resolutionLaserFraming (construction)Frame ratePhysicsMaterials scienceParametric statisticsPhotonicsOptoelectronics

Abstract

fetched live from OpenAlex

We report a framing imaging based on noncollinear optical parametric amplification (NCOPA), named FINCOPA, which applies NCOPA for the first time to single-shot ultrafast optical imaging. In an experiment targeting a laser-induced air plasma grating, FINCOPA achieved 50 fs-resolved optical imaging with a spatial resolution of ∼83 lp / mm and an effective frame rate of 10 trillion frames per second (Tfps). It has also successfully visualized an ultrafast rotating optical field with an effective frame rate of 15 Tfps. FINCOPA has simultaneously a femtosecond-level temporal resolution and frame interval and a micrometer-level spatial resolution. Combining outstanding spatial and temporal resolutions with an ultrahigh frame rate, FINCOPA will contribute to high-spatiotemporal resolution observations of ultrafast transient events, such as atomic or molecular dynamics in photonic materials, plasma physics, and laser inertial-confinement fusion.

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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.999

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)0.0020.000

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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