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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".