Study of a simplified CoolGal target to support the Phase-0 NEPIR facility
Bibliographic record
Abstract
The NEPIR facility (NEutron and Proton Irradiation), currently in development at the Laboratori Nazionali di Legnaro of the Istituto Nazionale di Fisica Nucleare (LNL-INFN), will serve as the first fast neutron ( E n > 1 MeV) irradiation source in Italy specifically designed to support advanced scientific research and industrial applications. Powered by the SPES (Selective Production of Exotic Species) variable-energy proton cyclotron, NEPIR will be implemented in two main phases. Phase-0 will deliver continuous (white spectrum) and pseudo quasi-monoenergetic neutron fields, while Phase-1 will introduce true quasi-monoenergetic neutron beams and an atmospheric-like neutron spectrum. The Phase-0 target system, CoolGal, is based on a thick beryllium target and is intended to evolve into a galinstan-cooled configuration for high-power operation. To simplify initial commissioning, a water-cooled prototype—excluding galinstan—has been developed. This paper presents thermal and structural analysis of the prototype and evaluates its neutron production performance through Monte Carlo simulations. The primary goal is to support an experiment to measure the double-differential neutron yield of the Be(p,n) reaction in the 30–70 MeV range. These results will provide new experimental data to supplement current Japanese Evaluated Nuclear Data Library evaluations and guide the development of advanced neutron sources at NEPIR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".