MétaCan
Menu
Back to cohort
Record W4405033604 · doi:10.48550/arxiv.2412.00808

Non-perturbative Effects in Attosecond Four-Wave Mixing Spectra

2024· preprint· en· W4405033604 on OpenAlexfundno aff
Sergio Yanez-Pagans, Nathan Harkema, Arvinder Sandhu, Coleman Cariker, Luca Argenti

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsnot available
FundersBasic Energy SciencesU.S. Department of EnergyOffice of ScienceCanadian Centre for Applied Research in Cancer ControlNational Science Foundation
KeywordsMixing (physics)AttosecondPhysicsSpectral lineNon-perturbativeQuantum electrodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

We study the nonlinear optical response of argon to a four-wave-mixing pulse sequence consisting of an extreme ultraviolet pulse, an overlapping collinear IR and an non-collinear delayed IR pulses. Absorption of an extreme ultraviolet and an IR photon from the collinear beams excites, sequentially, the $3s^{-1}4p$ bright state and the {$3s^{-1}3d/5s$} dark states. The subsequent absorption of an IR photon from the non-collinear beam results in an angled extreme ultraviolet emission whose variation with delay encodes coupling between autoionizing-states, dark-state lifetimes, and non-perturbative effects. Both our measurements and \emph{ab initio} simulations of the angled four-wave-mixing signal show a double-peak structure in delay dependence, in excellent agreement with each other. We attribute the minimum at the center of the signal to the rapid Rabi cycling, driven by the IR pulse, between dark states and the $3s^{-1}4p$ resonance, which results in the destructive interference in the final transition amplitude.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.198
Teacher spread0.156 · 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 routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicLaser-Matter Interactions and ApplicationsFrench-language works237,207