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Record W4283731421 · doi:10.1007/jhep04(2023)046

Heavy neutral leptons at beam dump experiments of future lepton colliders

2023· article· en· W4283731421 on OpenAlexaff
Pierce Giffin, Stefania Gori, Yu-Dai Tsai, Douglas Tuckler

Bibliographic record

VenueJournal of High Energy Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsCarleton University
FundersKavli Institute for Theoretical Physics, University of California, Santa BarbaraAspen Center for PhysicsHigh Energy PhysicsU.S. Department of EnergyUniversity of California, Santa BarbaraOffice of ScienceNational Science Foundation
KeywordsPhysicsLeptonColliderParticle physicsNuclear physicsBeam dumpLarge Hadron ColliderInternational Linear ColliderBeam (structure)PositronNeutrinoElectronCathode rayDetectorOptics

Abstract

fetched live from OpenAlex

Abstract A new beam dump experiment that utilizes the beam of future high energy electron-positron colliders could be an excellent avenue to search for dark sector particles due to its unprecedented high energy and intensity. We consider heavy neutral leptons (HNLs) as a specific example to demonstrate the sensitivity of searches for dark sector particles at future electron-positron collider beam dump experiments. This includes the study of the reach at the International Linear Collider (ILC), the Cool Copper Collider (C3), and the Compact Linear Collider (CLIC). We comprehensively examine the HNL production and detector acceptance at these electron beam dump experiments. We show that these experiments will probe regions of HNL parameter space, not yet probed by past experiments, as well as by future approved experiments. Our study also motivates a more detailed analysis of heavy meson productions in high-energy electron-nucleon collisions in thick targets.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.256
Teacher spread0.244 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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