Simulation of the Sudbury Neutrino Observatory neutral current detectors
Bibliographic record
Abstract
The Sudbury Neutrino Observatory (SNO), a heavy water Cherenkov experiment, was designed to detect solar Boron-8 neutrinos via their elastic scattering interactions on electrons, or charged current and neutral current (NC) interactions on deuterium. In the third phase of SNO, an array of Helium-3 proportional counters was deployed to detect neutrons produced in NC interactions. A simulation of the current pulses and energy spectra of the main kinds of ionization events inside these Neutral Current Detectors (NCDs) was developed. To achieve this, electron drift times in NCDs were evaluated with a Monte Carlo method, and constrained by using wire alpha activity inside the counters. The pulse calculation algorithm applies to any ionization event, and takes into account processes such as straggling, electron diffusion, and propagation through the NCD hardware. A space charge model was developed to fully explain the energy spectra of neutron and alpha events. Comparisons with data allowed the various classes of alpha backgrounds to be identified, and gave evidence for the spatial non-uniformity of Uranium-238 and Thorium-232 chain nuclei in the counter walls. The simulation was applied to determine the fractional contents of the main types of alpha backgrounds in each NCD string. The number of neutron capture events in the array was extracted via a statistical separation, using Monte Carlo generated alpha background pulse shape parameter distributions and minimal energy information. The inferred total Boron-8 solar neutrino flux is: ΦNC< = 5.74 ± 0.77 (stat) ± 0.39 (sys) x 106 cm-2s-1 in agreement with Standard Solar predictions and previous SNO results.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".