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Record W4388899819 · doi:10.1103/physrevd.108.095039

Dark photons from charged pion bremsstrahlung at proton beam experiments

2023· article· en· W4388899819 on OpenAlexafffund
David Curtin, Yonatan Kahn, Rachel Nguyen

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan FoundationUniversity of TorontoU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsBremsstrahlungPionPartonNuclear physicsParticle physicsPhotonProtonHadronElectron

Abstract

fetched live from OpenAlex

The production and subsequent rescattering of secondary pions produced in proton beam dumps provide additional opportunities for the production of light new particles such as dark photons. This new mechanism has been overlooked in the past but can extend the mass reach of the SpinQuest experiment and its proposed DarkQuest upgrade. We use chiral perturbation theory to calculate the production of kinetically mixed dark photons through bremsstrahlung off secondary charged pions. We find that the reach of SpinQuest/DarkQuest can be pushed further into the multi-GeV mass range compared to estimates based only on primary dark photon production through meson decay or proton bremsstrahlung. Our analysis can be regarded as the first of several steps to include secondary pion contributions. In an upcoming analysis we will extend our calculation into the high-momentum-transfer regime through the use of pion parton distribution functions and including hadronic resonances, which will further increase the estimated mass reach.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.427
Teacher spread0.403 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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