Toward the design of a very cold neutron source for the High Brilliance Source
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".