Bibliographic record
Abstract
The SNO+ experiment is the successor to the Nobel prize winning SNO experiment.SNO+ will ultimately search for neutrinoless double beta decay in 130 Te. 1.3 tonnes of 130 Te will be dissolved into 780 tonnes of organic liquid scintillator (LAB).9300 photo-multiplier tubes (PMTs) will observe the loaded scintillator held in an acrylic vessel of 6 m radius.This final stage of operation is proceeded by a number of preparation periods, during which other physics may be probed.This thesis reports the status of a physics measurement in the first phase of operation with a detector filled with ultra-pure water.The work aims to measure the production of cosmic muon induced neutrons that spallate in water and capture on hydrogen releasing a 2.2 MeVgamma ray.This process presents as a large background in many weak signal searches, such as dark matter searches, knowledge of the induced neutron production rate will inform both theoretical and experimental considerations.All components of the analysis developed to date are detailed.A low level PMT anomaly detection system is motivated and implemented, it is shown to remove high noise and inactive PMTs from data, applicable to general SNO+ analyses but especially important for low energy events.Muon event reconstruction is implemented, resulting in a track length error of ≤5% for tracks lengths > 10 m.A muon selection focused on a purity is detailed, producing a sample of muon candidates consistent with the rate observed | viii in SNO.A neutron candidate selection with a purity of ≈99% is also defined.After applying both selections to a custom data processing, the neutron capture time is measured to be 134 ± 16 µs which is inconsistent with the expected capture time of 206.03 ± 0.44 µs as measured by an 241 Am 9 Be calibration source in SNO+.This inconsistency is shown to be a result of electronic instability at short times after a cosmic muon event.This electronic instability is a previously unknown effect, leaveing the analysis incomplete and requiring future work.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".