MétaCan
Menu
Back to cohort
Record W1512460419 · doi:10.2172/891295

SIMULATION OF A WIDE-BAND LOW-ENERGY NEUTRINO BEAM FOR VERY LONG BASELINE NEUTRINO OSCILLATION EXPERIMENTS.

2006· report· en· W1512460419 on OpenAlexaff
M. Bishai, Jordan Heim, C. A. Lewis, A. D. Marino, B. Viren, F. Yumiceva

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsFermilabNeutrinoPhysicsNuclear physicsNeutrino oscillationBeam (structure)ProtonFermi Gamma-ray Space TelescopeBeamlineSynchrotronParticle acceleratorOptics

Abstract

fetched live from OpenAlex

We present simulations of a wide-band low-energy neutrino beam for a future very long baseline neutrino oscillation (VLBNO) program using the proton beam from the Main Injector (MI) proton accelerator at Fermi National Accelerator Laboratory (Fermilab). The target and horn designs previously developed for Brookhaven Laboratory's Alternating Gradient Synchrotron (AGS) VLBNO program are used without modifications. The neutrino flux distributions for various MI proton beam energies and new high-intensity neutrino beam-line designs possible at Fermilab are presented. The beam-line siting and design parameters are chosen to match the requirements of an on-axis beam from Fermilab to one of the two possible sites for the future Deep Underground Science and Engineering Laboratory (DUSEL). A preliminary estimate of the observable event rates and spectra at a detector located in DUSEL for different beam configurations has been performed. Our preliminary conclusions are that a 40-60 GeV 0.5 to 1 MW beam from the Fermilab Main Injector to a DUSEL site has the potential to reach the desired intensity for the next generation of neutrino oscillation experiments. Recent studies indicate that the Fermilab MI can reach a beam power of 0.5 MW at 60 GeV with incremental upgrades to the existing accelerator complex.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.038
GPT teacher head0.338
Teacher spread0.300 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicNeutrino Physics ResearchFrench-language works237,207