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Record W3201418995 · doi:10.1364/oe.430828

Sub-15-fs X-ray pump and X-ray probe experiment for the study of ultrafast magnetization dynamics in ferromagnetic alloys

2021· article· en· W3201418995 on OpenAlexaff
Xuan Liu, Alaaeldine Merhe, Emmanuelle Jal, Renaud Delaunay, Romain Jarrier, Valentin Chardonnet, Marcel Hennes, Katherine Légaré, M. Hennecke, Ilie Radu, Clemens von Korff Schmising, Særen Grunewald, Marion Kuhlmann, J. Lüning, Boris Vodungbo

Bibliographic record

VenueOptics Express · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsInstitut National de la Recherche Scientifique
FundersLaboratoire d’excellence Physique Atomes Lumière MatièreCentre National de la Recherche ScientifiqueBundesministerium für Bildung und ForschungAgence Nationale de la RechercheDeutsche Forschungsgemeinschaft
KeywordsPermalloyKerr effectOpticsUltrashort pulseMagnetization dynamicsFerromagnetismMaterials scienceMagnetizationDemagnetizing fieldFaraday effectMagneto-optic Kerr effectLaserCondensed matter physicsPhysicsMagnetic field

Abstract

fetched live from OpenAlex

In this paper, we present a new setup for the measurement of element-specific ultrafast magnetization dynamics in ferromagnetic thin films with a sub-15-fs time resolution. Our experiment relies on a split and delay approach which allows us to fully exploit the shortest X-rays pulses delivered by X-ray Free Electrons Lasers (close to the attosecond range), in an X-ray pump – X-ray probe geometry. The setup performance is demonstrated by measuring the ultrafast elemental response of Ni and Fe during demagnetization of ferromagnetic Ni and Ni 80 Fe 20 (Permalloy) samples upon resonant excitation at the corresponding absorption edges. The transient demagnetization process is measured in both reflection and transmission geometry using, respectively, the transverse magneto-optical Kerr effect (T-MOKE) and the Faraday effect as probing mechanisms.

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 score0.596
Threshold uncertainty score0.654

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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