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Record W2044193421 · doi:10.1103/physreva.83.063201

Clusters in intense XUV pulses: Effects of cluster size on expansion dynamics and ionization

2011· article· en· W2044193421 on OpenAlexaff
Edward Ackad, Nicolas Bigaouette, Kyle Briggs, Lora Ramunno

Bibliographic record

VenuePhysical Review A · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPhotoionizationExtreme ultravioletAtomic physicsIonizationCluster (spacecraft)ElectronKinetic energyPhysicsExcitationMolecular physicsLaserIonOptics

Abstract

fetched live from OpenAlex

We examine the effect of cluster size on the interaction of Ar${}_{55}$-Ar${}_{2057}$ with intense extreme ultraviolet (XUV) pulses, using a model we developed earlier that includes ionization via collisional excitation as an intermediate step. We find that the dynamics of these irradiated clusters is dominated by collisions. Larger clusters are more highly collisional, produce higher charge states, and do so more rapidly than smaller clusters. Higher charge states produced via collisions are found to reduce the overall photon absorption, since charge states of Ar${}^{2+}$ and higher are no longer photoaccessible. We call this mechanism collisionally reduced photoabsorption, and it decreases the effective cluster photoabsorption cross section by more than 30% for Ar${}_{55}$ and 45% for Ar${}_{2057}$. The time evolution of the electron kinetic energy distribution begins as a (mostly) Maxwellian distribution. Further, the electron velocity distribution of large clusters quickly become isotropic while smaller clusters retain the inherent anisotropy created by photoionization. Last, the total electron kinetic-energy distribution is integrated over the spatial profile of the laser and the log-normal distribution of cluster size for comparison with a recent experiment [C. Bostedt et al., Phys. Rev. Lett. 100, 133401 (2008)], and good agreement is found.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.360

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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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 designObservational
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

Citations12
Published2011
Admission routes1
Has abstractyes

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