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Record W4414100773 · doi:10.1016/j.optcom.2025.132450

Spatiotemporal couplings in spectrally multiplexed optical parametric amplifications

2025· article· en· W4414100773 on OpenAlexfundno aff
Qiwen Zhen, Xin Liu, Jinhui Li, Bobin Gao, Yishan Wang, Wei Zhao, Nan Huang, Huabao Cao, Yuxi Fu

Bibliographic record

VenueOptics Communications · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsnot available
FundersNatural Science Basic Research Program of Shaanxi ProvinceNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaYouth Innovation Promotion AssociationChinese Academy of SciencesOntario Physiotherapy Association
KeywordsOptical parametric amplifierUltrashort pulseParametric statisticsMultiplexingLaserChirped pulse amplificationPulse (music)SIGNAL (programming language)Strehl ratioPhase (matter)

Abstract

fetched live from OpenAlex

Ultrashort laser pulses with high peak intensity and good spatiotemporal characteristic are essential for attosecond pulse generation, laser-plasma acceleration and other strong-field physics research. The spectrally multiplexed optical parametric amplification (OPA) is one of the key approaches to generate pulses with high peak intensity and ultrashort pulse duration. To investigate the spatiotemporal characteristic of these ultrashort intense pulses, we present a numerical simulation of spectrally multiplexed noncollinear optical parametric amplification (NOPA) system. The spatiotemporal couplings (STCs) are caused by the inhomogeneous signal gain in spatial domain. When longer crystal is employed to achieve higher gain level, the difference of optical parametric phase (OPP) at distinct beam positions increases, leading to stronger STCs. At the same gain level, STCs in spectrally multiplexed NOPA can be effectively suppressed by adopting flat-top pump profile, where the Strehl Ratio (SR) of amplified signal is higher than that using Gaussian pump profile.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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