MétaCan
Menu
Back to cohort
Record W2936577819 · doi:10.1038/s41598-019-42433-1

An intense, few-cycle source in the long-wave infrared

2019· article· en· W2936577819 on OpenAlexaff
Derrek Wilson, Adam M. Summers, Stefan Zigo, Brandin Davis, Seyyed-Javad Robatjazi, J. A. Powell, Daniel Rolles, Artem Rudenko, Carlos Trallero–Herrero

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersChemical Sciences, Geosciences, and Biosciences DivisionBasic Energy SciencesNational Defense Science and Engineering GraduateU.S. Department of EnergyU.S. Department of DefenseOffice of ScienceOffice of Experimental Program to Stimulate Competitive ResearchNational Science Foundation
KeywordsIonizationInfraredWavelengthExcitationXenonPhysicsAtomic physicsLaserOptoelectronicsOpticsIonQuantum mechanics

Abstract

fetched live from OpenAlex

For the last several decades, the wavelength range accessible for strong-field, few-cycle studies has remained limited to the visible, near infrared and mid-wave infrared regimes. In particular, sources in the long-wave infrared have been lacking. We report the development of a 1 kHz, few-cycle laser source with up to a 9 μm central wavelength and gigawatt peak powers. When focused, this source can ionize gas targets, which we demonstrate here through the ionization of atomic xenon at wavelengths ranging from 5 μm to 9 μm. This opens up new opportunities for fundamental atomic and molecular physics, enabling experimental tests of strong-field ionization theories in the extreme long-wavelength, few-cycle limit and the direct excitation of vibrational transitions in organic molecules.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
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)0.0010.000

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 head0.255
Teacher spread0.245 · 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.

Study designObservational
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

Citations25
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicLaser-Matter Interactions and ApplicationsFrench-language works237,207