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Record W4405222732 · doi:10.1088/2515-7647/ad9cdb

Characterizing ultrashort pulses with photon energies above 1.12 eV based on transient absorption in silicon thin films

2024· article· en· W4405222732 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

VenueJournal of Physics Photonics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsJoint Attosecond Science LaboratoryInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsTransient (computer programming)SiliconMaterials scienceAbsorption (acoustics)Two-photon absorptionUltrafast laser spectroscopyPhotonOpticsOptoelectronicsUltrashort pulseAtomic physicsPhysicsLaserComputer science

Abstract

fetched live from OpenAlex

Abstract Frequency-resolved optical switching (FROSt) is a phase-matching-free characterization technique for ultrashort pulses based on transient absorption in semiconductors. So far, this technique has been limited to characterizing pulses with photon energies smaller than the bandgap of the semiconductors used. In this work, we extend the method to characterize pulses of photon energy greater than the bandgap of the semiconductor used for characterization. We demonstrate this by characterizing ultrashort visible pulses and supercontinuum using silicon (Si) thin films deposited on a sapphire substrate. We also demonstrate that visible light sources up to a repetition rate of 250 kHz can be characterized using these samples. Therefore, this study highlights the potential of FROSt as a suitable technique for the temporal characterization of weak visible to infrared pulses, including high harmonics generated in solids.

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 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 score0.017
Threshold uncertainty score0.637

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)0.0000.000

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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