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Record W4226012905 · doi:10.48550/arxiv.2203.05010

New Ideas in Baryogenesis: A Snowmass White Paper

2022· preprint· en· W4226012905 on OpenAlexfundno aff
Gilly Elor, Julia Harz, Seyda Ipek, Bibhushan Shakya, Nikita Blinov, Raymond T. Co, Yanou Cui, Arnab Dasgupta, Hooman Davoudiasl, Fatemeh Elahi, Kåre Fridell, Akshay Ghalsasi, Keisuke Harigaya, Chandan Hati, Peisi Huang, Azadeh Maleknejad, Robert McGehee, David E. Morrissey, Kai Schmitz, M. Shamma, Brian Shuve, David Tucker-Smith, Jorinde van de Vis, Graham White

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungMinistry of Education, Culture, Sports, Science and TechnologyDeutsche ForschungsgemeinschaftTRIUMFUniversity of MinnesotaU.S. Department of EnergyNational Science Foundation
KeywordsBaryogenesisPhysicsPhysics beyond the Standard ModelBaryon asymmetryAsymmetryStandard Model (mathematical formulation)Particle physicsSign (mathematics)TestabilityBaryonCP violationBaryon numberTheoretical physicsRange (aeronautics)Nuclear physicsEngineeringEpistemologyMathematicsAerospace engineeringPhilosophyGauge (firearms)

Abstract

fetched live from OpenAlex

The Standard Model of Particle Physics cannot explain the observed baryon asymmetry of the Universe. This observation is a clear sign of new physics beyond the Standard Model. There have been many recent theoretical developments to address this question. Critically, many new physics models that generate the baryon asymmetry have a wide range of repercussions for many areas of theoretical and experimental particle physics. This white paper provides an overview of such recent theoretical developments with an emphasis on experimental testability.

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)0.002
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: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.187
Teacher spread0.159 · 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
GenreReview

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

Citations4
Published2022
Admission routes1
Has abstractyes

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