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Record W2889770703 · doi:10.1007/jhep01(2019)074

Limits from BBN on light electromagnetic decays

2019· article· en· W2889770703 on OpenAlexafffund

Bibliographic record

VenueJournal of High Energy Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersNatural Sciences and Engineering Research Council of CanadaCurtin University of TechnologyTRIUMFAspen Center for PhysicsNational Science Foundation
KeywordsElectroweak interactionPhotonAnnihilationElectromagnetic radiationBig Bang nucleosynthesisElectromagnetic spectrumNucleosynthesisCascadeElectromagnetic fieldElectron

Abstract

fetched live from OpenAlex

A bstract Injection of electromagnetic energy — photons, electrons, or positrons — into the plasma of the early universe can destroy light elements created by primordial Big Bang Nucleosynthesis (BBN). The success of BBN at predicting primordial abundances has thus been used to impose stringent constraints on decay or annihilation processes with primary energies near or above the electroweak scale. In this work we investigate the constraints from BBN on electromagnetic decays that inject lower energies, between 1–100 MeV. We compute the electromagnetic cascade from such injections and we show that it can deviate significantly from the universal spectrum commonly used in BBN calculations. For electron injection below 100 MeV, we find that the final state radiation of photons can have a significant impact on the resulting spectrum relevant for BBN. We also apply our results on electromagnetic cascades to investigate the limits from BBN on light electromagnetic decays prior to recombination, and we compare them to other bounds on such decays.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.216
Teacher spread0.209 · 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

Citations54
Published2019
Admission routes2
Has abstractyes

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