MétaCan
Menu
Back to cohort
Record W3015490836 · doi:10.3929/ethz-b-000445433

Study of nuclear properties with muonic atoms

2020· preprint· en· W3015490836 on OpenAlexfundno aff
A. Knecht, A. Skawran, Stergiani Marina Vogiatzi

Bibliographic record

VenuearXiv (Cornell University) · 2020
Typepreprint
Languageen
FieldEngineering
TopicMuon and positron interactions and applications
Canadian institutionsnot available
FundersGSI Helmholtzzentrum für SchwerionenforschungInstitut Périmètre de physique théoriqueJohannes Gutenberg-Universität MainzUniversità di PisaKU LeuvenEidgenössische Technische Hochschule ZürichBundesministerium für Bildung und ForschungUniversität zu KölnPaul Scherrer InstitutRijksuniversiteit GroningenSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMuonExotic atomAtomic physicsPhysicsNuclear physicsQuadrupoleElectronDeuteriumEffective nuclear charge

Abstract

fetched live from OpenAlex

Muons are a fascinating probe to study nuclear properties. Muonic atoms can easily be formed by stopping negative muons inside a material. The muon is subsequently captured by the nucleus and, due to its much higher mass compared to the electron, orbits the nucleus at very small distances. During this atomic capture process the muon emits characteristic X-rays during its cascade down to the ground state. The energies of these X-rays reveal the muonic energy level scheme, from which properties like the nuclear charge radius or its quadrupole moment can be extracted. While almost all stable elements have been examined using muons, probing highly radioactive atoms has so far not been possible. The muX experiment has developed a technique based on transfer reaction inside a high pressure hydrogen/deuterium gas cell to examine targets available only in microgram quantities.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.558

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 head0.079
GPT teacher head0.169
Teacher spread0.090 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicMuon and positron interactions and applicationsFrench-language works237,207