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Record W4416010444 · doi:10.1109/tasc.2025.3630163

Magnetic Field Mapping Design for the MOLLER Spectrometer Magnets at Jefferson Lab

2025· article· W4416010444 on OpenAlexaff
Probir K. Ghoshal, B. Eng, S. Gopinath, J. Lamont, Michael Dion, Ruben Fair, D. Kashy, J. Mammei, S. Rahman, Renuka Rajput-Ghoshal, E. Sun, D. Young

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2025
Typearticle
Language
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSpectrometerMagnetSuperconducting magnetToroidMagnetic fieldCalibrationElectronBeam (structure)Particle accelerator

Abstract

fetched live from OpenAlex

The Thomas Jefferson National Accelerator Facility (JLab) has developed a unique spectrometer system to study the weak interaction between electrons. The “Measurement of Lepton-Lepton Electroweak Reaction” (MOLLER) experiment, utilizing JLab's recent 12 GeV electron beam upgrade, is scheduled to operate for three years. Central to the MOLLER experiment are five water-cooled toroidal magnets, each with a unique geometry and seven-fold symmetry, designed to focus the particles. The magnetic field generated by the Spectrometer is needed to separate the incident beam electrons that scatter from target electrons (Moller scattering) from the protons due to elastic e-p scattering within a liquid hydrogen target. This paper details the magnet field measuring technique developed to map all five MOLLER toroidal magnets at multiple locations inside and along the bore. It covers the design, mounting, and operation of the probe, along with the calibration procedure to determine the field and to prepare a field map for GEANT4 analysis. Additionally, the paper addresses the challenges of accurately measuring low magnetic fields

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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: none
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.244
Teacher spread0.208 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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