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Record W4387325891 · doi:10.1063/5.0160783

Stable high repetition-rate laser-driven proton beam production for multidisciplinary applications on the advanced laser light source ion beamline

2023· article· en· W4387325891 on OpenAlexafffund
Elias Catrix, Frédéric Boivin, Kassandra Langlois, Simon Vallières, Canan Yağmur Boynukara, S. Fourmaux, P. Antici

Bibliographic record

VenueReview of Scientific Instruments · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsPolytechnique MontréalUniversity of WaterlooInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBeamlineLaserProtonIonOpticsMaterials scienceBeam (structure)Ion beamRepetition (rhetorical device)Laser beamsOptoelectronicsAtomic physicsNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Laser-driven proton accelerators are relevant candidates for many applications such as material science or medicine. Today, there are multi-hundred-TW table-top laser systems that can generate relativistic peak intensities >1018 W/cm2 and routinely reach proton energies in the MeV range. However, for most desired applications, there is still a need to optimize the quality and stability of the laser-generated proton beam. In this work, we developed a 0.625 Hz high repetition-rate setup in which a laser with 2.5% RMS energy stability is irradiating a solid target with an intensity of 1019 to 1020 W/cm2 to explore proton energy and yield variations, both with high shot statistics (up to about 400 laser shots) and using different interaction targets. Investigating the above-mentioned parameters is important for applications that rely on specific parts of the proton spectrum or a high ion flux produced over quick multi-shot irradiation. We demonstrate that the use of a stable "multi-shot mode" allows improving applications, e.g., in the detection of trace elements using laser-driven particle-induced x-ray emission.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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 designNot applicable
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

Citations10
Published2023
Admission routes2
Has abstractyes

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