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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 OpenAlexafffund
Mayank Kumar, Saadat Mokhtari, Tristan Guay, Adrien Leblanc, Kosta Oubrerie, Sohail A. Jalil, Elissa Haddad, Gaëtan Jargot, Philippe Lassonde, Heide Ibrahim, Giulio Vampa, François Légaré

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
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

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

Citations0
Published2024
Admission routes2
Has abstractyes

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