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Record W1987952828 · doi:10.1364/ao.44.000391

Long-range detection and length estimation of light filaments using extra-attenuation of terawatt femtosecond laser pulses propagating in air

2005· article· en· W1987952828 on OpenAlexaff
Qi Luo, Jin Yu, Sima Hosseini, Weiwei Liu, B. Ferland, G. Roy, See Leang Chin

Bibliographic record

VenueApplied Optics · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsFilamentationOpticsFemtosecondLaserSupercontinuumAttenuation coefficientAttenuationMaterials scienceFemtosecond pulse shapingSelf-phase modulationTunnel ionizationIonizationPhysicsWavelengthNonlinear optics

Abstract

fetched live from OpenAlex

High-energy femtosecond laser pulses propagating in the atmosphere undergo self-focusing resulting in the appearance of the phenomenon of filamentation. We observed an extra-attenuation of such (terawatt) femtosecond laser pulses propagating in the atmosphere when compared with long pulses (200 ps) with the same energy. This is because, in contrast to the linear propagation of the long pulse, the input femtosecond laser pulse is attenuated owing to either absorption through multiphoton ionization/tunnel ionization or to scattering on the laser-induced plasma; self-phase-modulation and self-steepening further convert partially the energy initially contained in the fundamental bandwidth into the broad side bands of the laser, becoming eventually a white-light laser pulse (supercontinuum). The experimental data allow us to extract an effective extra-attenuation coefficient for an exponential decay of the input pulse energy with the propagation distance. Such a coefficient allows us to estimate an upper bound of the filament length under the experimental conditions used. More generally, our observation leads to a new technique to remotely detect light filaments in the atmosphere.

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 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 score0.311
Threshold uncertainty score0.382

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 head0.013
GPT teacher head0.258
Teacher spread0.246 · 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 teacher head, 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

Citations22
Published2005
Admission routes1
Has abstractyes

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