MétaCan
Menu
Back to cohort

Millicharged Particles in Neutrino Experiments

2019· article· en· W2807315164 on OpenAlexafffund
Gabriel Magill, Ryan Plestid, Maxim Pospelov, Yu-Dai Tsai

Bibliographic record

VenuePhysical Review Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsUniversity of VictoriaMcMaster UniversityPerimeter Institute
FundersSLAC National Accelerator LaboratoryNatural Sciences and Engineering Research Council of CanadaInstitut Périmètre de physique théoriqueNational Science FoundationGovernment of CanadaFermilabU.S. Department of EnergyOffice of Science
KeywordsMiniBooNEPhysicsFermilabNeutrinoParticle physicsNuclear physicsParameter spaceColliderRange (aeronautics)Electron neutrinoSensitivity (control systems)Neutrino detectorFlaggingSterile neutrinoNeutrino oscillationAerospace engineering

Abstract

fetched live from OpenAlex

We set constraints and future sensitivity projections on millicharged particles (MCPs) based on electron scattering data in numerous neutrino experiments, starting with MiniBooNE and the Liquid Scintillator Neutrino Detector (LSND). Both experiments are found to provide new (and leading) constraints in certain MCP mass windows: 5-35 MeV for LSND and 100-180 MeV for MiniBooNE. Furthermore, we provide projections for the ongoing Fermilab SBN program, the Deep Underground Neutrino Experiment (DUNE), and the proposed Search for Hidden Particles (SHIP) experiment. In the SBN program, SBND and MicroBooNE have the capacity to provide the leading bounds in the 100-300 MeV mass regime. DUNE and SHIP are capable of probing parameter space for MCP masses in the range of 5 MeV-5 GeV that is significantly beyond the reach of existing bounds, including those from collider searches and, in the case of DUNE, the SLAC mQ experiment.

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)0.009
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.304
Teacher spread0.286 · 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

Citations123
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review LettersSame topicParticle physics theoretical and experimental studiesFrench-language works237,207