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Record W1976285612 · doi:10.1117/12.839825

Parametric investigation of laser wakefield acceleration versus F-number of focusing optics

2009· article· en· W1976285612 on OpenAlexaff
Navid Vafaei-Najafabadi, A. W. Ali, J. A. Chakera, R. Fedosejevs, Ying Y. Tsui

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsLaserAccelerationOpticsElectronAtomic physicsRayleigh lengthParabolaPlasmaHeliumPlasma accelerationNuclear physicsLaser beams

Abstract

fetched live from OpenAlex

Laser wakefield acceleration is a growing area of research with the promise of generating high energy, low divergence, and short duration electron bunches from tabletop scale accelerators. To date, electron beams with maximum energy of 1 GeV with 2.5% energy spread have been generated using a 3cm plasma channel [1] . However in order to advance the maximum energy of electron beams beyond this limit, better understanding of the physics and effect of different parameters on the interaction are essential. In this paper we report on our parametric studies of wakefield electron acceleration using the 10TW chirped pulse amplified laser system at the Advanced Laser Light Source (ALLS), Montreal. Laser pulses with energies of ~210 mJ at 33fs were focused using a short (f/6) and a long focal length (f/12) off axis parabola onto 2mm supersonic helium and nitrogen gas jets at different pressures. Nitrogen with electron densities of up to 2×10 20 cm -3 and helium densities up to 5×10 19 were used. Beams with energies of tens of MeV were observed using the short focal length parabola and beams with energies of several MeV were observed using the long focal length parabola. We also found that electron beams are more easily generated with higher levels of prepulse, consistent with previous reports of prepulse generated guiding channels in the plasma[5].

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.764

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 communication0.0000.001
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicLaser-Plasma Interactions and DiagnosticsFrench-language works237,207