MétaCan
Menu
Back to cohort
Record W2039914736 · doi:10.1088/1367-2630/14/6/065011

Fully microscopic analysis of laser-driven finite plasmas using the example of clusters

2012· article· en· W2039914736 on OpenAlexaff
Christian Peltz, Charles Varin, Thomas Brabec, Thomas Fennel

Bibliographic record

VenueNew Journal of Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of Ottawa
FundersDeutsche Forschungsgemeinschaft
KeywordsPhysicsPlasmaLaserAttosecondPlasmonComputational physicsField (mathematics)OpticsUltrashort pulseQuantum mechanics

Abstract

fetched live from OpenAlex

We discuss a microscopic particle-in-cell (MicPIC) approach that allows bridging of the microscopic and macroscopic realms of laser-driven plasma physics. The simultaneous resolution of collisions and electromagnetic field propagation in MicPIC enables the investigation of processes that have been inaccessible to rigorous numerical scrutiny so far. This is illustrated by the two main findings of our analysis of pre-ionized, resonantly laser-driven clusters, which can be realized experimentally in pump–probe experiments. In the linear response regime, MicPIC data are used to extract the individual microscopic contributions to the dielectric cluster response function, such as surface and bulk collision frequencies. We demonstrate that the competition between surface collisions and radiation damping is responsible for the maximum in the size-dependent lifetime of the Mie surface plasmon. The capacity to determine the microscopic underpinning of optical material parameters opens new avenues for modeling nano-plasmonics and nano-photonics systems. In the non-perturbative regime, we analyze the formation and evolution of recollision-induced plasma waves in laser-driven clusters. The resulting dynamics of the electron density and local field hot spots opens a new research direction for the field of attosecond science.

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 score0.656
Threshold uncertainty score0.246

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)0.0000.000

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 head0.038
GPT teacher head0.301
Teacher spread0.264 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueNew Journal of PhysicsSame topicLaser-Matter Interactions and ApplicationsFrench-language works237,207