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Search for Dark-Matter-Induced Oscillations of Fundamental Constants Using Molecular Spectroscopy

2022· article· en· W4285492914 on OpenAlexfundno aff
René Oswald, A. Nevsky, Victor Vogt, S. Schiller, Nataniel L. Figueroa, Ke Zhang, Oleg Tretiak, D. Antypas, Dmitry Budker, Abhishek Banerjee, Gilad Pérez

Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersH2020 European Research CouncilMaurice and Vivienne Wohl Charitable FoundationAzrieli FoundationIsrael Science FoundationHorizon 2020 Framework ProgrammeDeutsche ForschungsgemeinschaftEuropean CommissionUnited States-Israel Binational Science FoundationNational Science Foundation
KeywordsPhysicsOscillation (cell signaling)Dark matterCoupling (piping)Coupling constantParameter spaceStandard Model (mathematical formulation)Field (mathematics)SpectroscopyDegrees of freedom (physics and chemistry)Particle physicsQuantum mechanics

Abstract

fetched live from OpenAlex

A possible implication of an ultralight dark matter field interacting with the standard model degrees of freedom is oscillations of fundamental constants. Here, we establish direct experimental bounds on the coupling of an oscillating ultralight dark matter field to the up, down, and strange quarks and to the gluons, for oscillation frequencies between 10 and 10^{8} Hz. We employ spectroscopic experiments that take advantage of the dependence of molecular transition frequencies on the nuclear masses. Our results apply to previously unexplored frequency bands and improve on existing bounds at frequencies >5 MHz. We also improve on the bounds for coupling to the electromagnetic field and the electron field, in particular spectral windows. We identify a sector of ultralight dark matter and standard model coupling space where the bounds from equivalence principle tests may be challenged by next-generation experiments of the present kind.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.567

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 head0.028
GPT teacher head0.324
Teacher spread0.295 · 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

Citations56
Published2022
Admission routes1
Has abstractyes

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