MétaCan
Menu
Back to cohort
Record W4363673702 · doi:10.1103/physreva.107.043107

Effects of tampering on rare-gas core-shell clusters irradiated by resonantly tuned soft-x-ray pulses

2023· article· en· W4363673702 on OpenAlexaff
Rishi Pandit, Valerie R. Becker, Jeremy Thurston, Kasey Barrington, Zachary Hartwick, Nicolas Bigaouette, Lora Ramunno, Edward Ackad

Bibliographic record

VenuePhysical review. A/Physical review, A · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsUniversity of Ottawa
FundersAir Force Office of Scientific ResearchNational Science Foundation
KeywordsXenonAtomic physicsIonizationArgonIonElectronResonance (particle physics)PhysicsCharge (physics)LaserKryptonNuclear physics

Abstract

fetched live from OpenAlex

Tampering resonant rare-gas clusters with nonresonant atoms has been shown to significantly decrease in the final ionization of the resonant atoms [J. Phys. B: At., Mol. Opt. Phys. 41, 181001 (2008)], but not their transient ionization [Phys. Rev. Lett. 112, 183401 (2014)]. In this paper, we explain the details of the charge transfer mechanism from the resonant to nonresonant atoms. We have applied our model to the interaction of an ultraintense x-ray laser tuned to the center of xenon's giant 4d resonance with core-shell argon-xenon clusters. Our results are in agreement with previous experimental results of the disintegration products, and the transient states. Also, our model predicts that the resonant xenon is quickly ionized by the laser. The freed electrons then collisionally ionized the outer argon atoms before recombining with the inner xenon ions to reduce their final charge state. We find that unlike homogeneous clusters whose behavior is governed by the number of atoms, the behavior of core-shell heterogeneous clusters is well predicted by the ratio of resonant to nonresonant atoms.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.260
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.319
Teacher spread0.308 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePhysical review. A/Physical review, ASame topicAtomic and Molecular PhysicsFrench-language works237,207