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Hot Electrons Transverse Refluxing in Ultraintense Laser-Solid Interactions

2010· article· en· W2056886823 on OpenAlexaff
S. Buffechoux, J. Pšikal, M. Nakatsutsumi, L. Romagnani, А. А. Андреев, Karl Zeil, M. Amin, P. Antici, Trevor Burris-Mog, A. Compant-La-Fontaine, E. d’Humières, S. Fourmaux, Ségolène Gaillard, F. Gobet, F. Hannachi, Stephan Kraft, A. Mančić, C. Plaisir, G. Sarri, M. Tarisien, T. Toncian, U. Schramm, M. Tampo, P. Audebert, O. Willi, T. E. Cowan, H. Pépin, V. T. Tikhonchuk, M. Borghesi, J. Fuchs

Bibliographic record

VenuePhysical Review Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsInstitut National de la Recherche Scientifique
FundersEngineering and Physical Sciences Research CouncilAgence Nationale de la Recherche
KeywordsPhysicsElectronAtomic physicsTransverse planeLaserIonProtonCoupling (piping)HomogeneousPulse (music)Order (exchange)AccelerationNuclear physicsOpticsMaterials scienceQuantum mechanics

Abstract

fetched live from OpenAlex

We have analyzed the coupling of ultraintense lasers (at ∼2×10{19} W/cm{2}) with solid foils of limited transverse extent (∼10 s of μm) by monitoring the electrons and ions emitted from the target. We observe that reducing the target surface area allows electrons at the target surface to be reflected from the target edges during or shortly after the laser pulse. This transverse refluxing can maintain a hotter, denser and more homogeneous electron sheath around the target for a longer time. Consequently, when transverse refluxing takes places within the acceleration time of associated ions, we observe increased maximum proton energies (up to threefold), increased laser-to-ion conversion efficiency (up to a factor 30), and reduced divergence which bodes well for a number of applications.

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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.308
Teacher spread0.296 · 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

Citations116
Published2010
Admission routes1
Has abstractyes

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