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Record W4412798310 · doi:10.1139/cjp-2025-0093

Toward the design of a very cold neutron source for the High Brilliance Source

2025· article· en· W4412798310 on OpenAlexvenueno aff
Dalini Maharaj, Ulrich Rücker, Jingjing Li, J. Voigt, Thomas Gutberlet, Paul Zakalek

Bibliographic record

VenueCanadian Journal of Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsNeutron sourceLight sourceOpticsNeutronNuclear physics

Abstract

fetched live from OpenAlex

In contrast to the moderator designs typical of large reactor and spallation sources, high-current accelerator-driven neutron sources, with smaller source dimensions, necessitate highly efficient and compact moderator solutions. At the High Brilliance Source (HBS), a hydrogen-rich moderator is required to effectively slow neutrons to the very cold neutron (VCN) energy range within the limited volume that aligns with the HBS target size. Methane, a well-established and highly efficient neutron moderator is a promising candidate to serve as a VCN moderator since it possesses a desirable low-lying rotor mode at ∼1 meV to facilitate neutron slowing. Liquid parahydrogen (pH 2 ) is a known efficient cold neutron moderator since it is able to convert thermal neutrons to cold neutrons via a single interaction. A geometrical configuration combining methane, embedded in pH 2 has been investigated to harness the complementary properties of both materials as a potential VCN moderator design for the HBS. Monte Carlo simulations using the Particle and Heavy Ion Transport code System particle transport code were conducted to evaluate the performance of the combined moderator concept when compared with a pure, low-dimensional pH 2 cold source.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.323

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)0.0000.000

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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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