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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 teacher head, not a consensus.

Study designBench or experimental
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

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