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Record W4405767740 · doi:10.48550/arxiv.2412.17276

Comagnetometry using mirror-symmetric ions in a crystal

2024· preprint· en· W4405767740 on OpenAlexfundno aff
Bassam Nima, Mingyu Fan, Aleksandar Radak, Andrew M. Jayich, Amar C. Vutha

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaJohn Templeton FoundationAlfred P. Sloan Foundation
KeywordsIonCrystal (programming language)CrystallographyPhysicsMaterials scienceChemistryOpticsComputer scienceQuantum mechanicsProgramming language

Abstract

fetched live from OpenAlex

Searches for physics beyond the Standard Model using spin sensors are susceptible to spurious frequency shifts and noise due to magnetic fields. Therefore a comagnetometer -- an auxiliary sensor that allows mundane magnetic field effects to be differentiated from new physics -- is an essential feature of many precision searches. Here we demonstrate the operation of a novel type of comagnetometer using nuclear spins of dopant ions in a crystal, comparing four different sub-ensembles of ions. We demonstrate rejection of magnetic-field-induced shifts to better than 1 part in 10$^5$ using this system, laying the groundwork for improved searches of time-reversal symmetry violation using solid-state systems.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.214
Teacher spread0.142 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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