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Record W4362501710 · doi:10.1051/epjconf/202328201017

Towards an electrostatic storage ring for fundamental physics measurements

2023· article· en· W4362501710 on OpenAlexaff
Chiara Brandenstein, Stefan Stelzl, E. Gutsmiedl, W. Schott, Andreas Weiler, P. Fierlinger

Bibliographic record

VenueEPJ Web of Conferences · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsPhysicsStorage ringDipoleCoherence (philosophical gambling strategy)ElectronAtomic physicsIonDark matterAxionBunchesPhotonNucleonNuclear physicsParticle physicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

We describe a new table-top electrostatic storage ring concept for 30 keV polarized ions with fixed spin orientation. The device will ultimately be capable of measuring magnetic fields with a resolution of 10−20 T with sub-mHz bandwidth. With the possibility to store different kinds of ions or ionic molecules and access to prepare and probe states of the systems using lasers and SQUIDs, it can be used to search for electric dipole moments (EDMs) of electrons and nucleons, as well as axion-like particle dark matter and dark photon dark matter. Its sensitivity potential stems from several hours of storage time, comparably long spin coherence times, and the possibility to trap up to 109 particles in bunches with possibly different state preparations for differential measurements. As a dark matter experiment, it is most sensitive in the mass range of 10−10 to 10−19 eV, where it can potentially probe couplings orders of magnitude below current and proposed laboratory experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.004

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 head0.056
GPT teacher head0.299
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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