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Relativistic Electrons from Vacuum Laser Acceleration Using Tightly Focused Radially Polarized Beams

2024· article· en· W4403204499 on OpenAlexafffund
J. A. Powell, Spencer W. Jolly, Simon Vallières, François Fillion‐Gourdeau, S. Payeur, S. Fourmaux, Marianna Lytova, Michel Piché, Heide Ibrahim, Steve MacLean, François Légaré

Bibliographic record

VenuePhysical Review Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
FundersH2020 Marie Skłodowska-Curie ActionsHorizon 2020 Framework ProgrammeFonds De La Recherche Scientifique - FNRSCanada Foundation for InnovationAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaMinistère de l'Économie, de l’Innovation et des Exportations du Québec
KeywordsPhysicsElectronAccelerationLaserAtomic physicsOpticsNuclear physicsClassical mechanics

Abstract

fetched live from OpenAlex

We generate a tabletop pulsed relativistic electron beam at 100 Hz repetition rate from vacuum laser acceleration by tightly focusing a radially polarized beam into a low-density gas. We demonstrate that strong longitudinal electric fields at the focus can accelerate electrons up to 1.43 MeV by using only 98 GW of peak laser power. The electron energy is measured as a function of laser intensity and gas species, revealing a strong dependence on the atomic ionization dynamics. These experimental results are supported by numerical simulations of particle dynamics in a tightly focused configuration that take ionization into consideration. For the range of intensities considered, it is demonstrated that atoms with higher atomic numbers like krypton can favorably inject electrons at the peak of the laser field, resulting in higher energies and an efficient acceleration mechanism that reaches a significant fraction (≈14%) of the theoretical energy gain limit.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

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)0.0010.001

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2024
Admission routes2
Has abstractyes

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