A new tool for (α,n) yield calculations and its implications for DEAP-3600
Bibliographic record
Abstract
Neutron-induced nuclear recoils present one of the dominant backgrounds in many low-background experiments. These neutrons are largely radiogenic in origin, coming from fission and (α, n) reactions in detector components. The (α, n) neutron production rate in a material depends on its composition and the energies of the α decays inside of it. We present NeuCBOT, the Neutron Calculator Based on TALYS, a new tool for calculating the (α, n) yields and neutron energy spectra for materials exposed to a given set of α energies or α-emitting isotopes. We benchmark these calculations against measured yields and SOURCES-4C calculations. We discuss these yield calculations in the context of DEAP-3600, a Weakly Interactive Massive Particle dark matter detector at SNOLAB. Using NeuCBOT and Geant4 simulations, we predict the neutron-induced nuclear recoil background in DEAP-3600. We then present in-situ constraints on the neutron background rate in DEAP-3600, consistent with our prediction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".