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Record W4386120458 · doi:10.1142/s0217751x23300120

Axions beyond Gen 2

2023· article· en· W4386120458 on OpenAlexaff
C. Boutan, G. Carosi, L. J. Rosenberg, G. Rybka, K. M. Backes, C. Bartram, Masha Baryakhtar, M.D. Bird, C. Braggio, Dmitry Budker, Raymond T. Co, E. J. Daw, Akash Dixit, Andrew Geraci, C. Lee, Soohyung Lee, David J. E. Marsh, Ciaran A. J. O’Hare, Ken’ichi Saikawa, Chiara P. Salemi, Yannis K. Semertzidis, A. Sonnenschein, Aaron Spector, Michael E. Tobar, Julia K. Vogel, Ariel Zhitnitsky

Bibliographic record

VenueInternational Journal of Modern Physics A · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of British Columbia
FundersPacific Northwest National LaboratoryLawrence Livermore National LaboratoryHeising-Simons FoundationUniversity of WashingtonBattelleU.S. Department of Energy
KeywordsAxionPhysicsParticle physicsDark matterQuantum chromodynamicsStrong CP problemSensitivity (control systems)

Abstract

fetched live from OpenAlex

The QCD (Quantum ChromoDynamics) axion emerged as one of the best-motivated dark matter candidates. In 2018, the Axion Dark Matter eXperiment (ADMX), one of the U.S. Department of Energy’s “Gen 2” flagship dark-matter projects, demonstrated first sensitivity to the highly plausible “DFSZ” dark matter axion couplings over a small frequency range. We anticipate this development marks the first step in constructing yet more powerful experiments that can explore large swaths of the axion parameter space at high sensitivity and result in a discovery. But, realizing this requires advances in both our understanding of the theory and experiment design. Between 25 January and 27 January 2021, the “Axions Beyond Gen 2 Workshop” was held, where selected members of the community discussed our broad understanding of the QCD axion and charted a course for future experiments having sensitivity and mass reach well beyond the current “Gen 2” experiments. These proceedings are summaries of the topics presented and discussed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.307

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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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