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Record W4380083110 · doi:10.1103/physrevd.107.115011

From axion quality and naturalness problems to a high-quality <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi mathvariant="double-struck">Z</mml:mi><mml:mi>N</mml:mi></mml:msub></mml:math> QCD relaxion

2023· article· lv· W4380083110 on OpenAlexfundno aff
Abhishek Banerjee, Joshua Eby, Gilad Pérez

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2023
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersMainz Institute for Theoretical Physics, Johannes Gutenberg University MainzAzrieli FoundationIsrael Science FoundationNational Science FoundationGerman-Israeli Foundation for Scientific Research and DevelopmentMaurice and Vivienne Wohl Charitable FoundationAspen Center for PhysicsJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyMinerva Foundation
KeywordsNaturalnessQuality (philosophy)Computer sciencePhysicsDatabaseParticle physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We highlight general issues associated with quality and naturalness problems in theories of light QCD axions, axionlike particles, and relaxions. We show that the presence of Planck-suppressed operators generically lead to scalar coupling of axions with the Standard Model. We present a new class of ${\mathbb{Z}}_{N}$ QCD relaxion models that can address both the QCD relaxion $CP$ problem as well as its quality problem. This new class of models also leads to interesting experimental signatures, which can be searched for at the precision frontier.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0030.007
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.025
GPT teacher head0.337
Teacher spread0.312 · 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 designTheoretical or conceptual
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

Citations19
Published2023
Admission routes1
Has abstractyes

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