Neutron multiplicity in atmospheric neutrino events at the Sudbury Neutrino Observatory
Bibliographic record
Abstract
This thesis describes the results of two separate analyses. Part I is the description of the first analysis which uses the newest measurements of neutrino mixing to study various non-standard models of neutrino interactions through their impact on solar neutrinos. These models can be motivated by the fact that solar neutrino experiments have yet to see directly the transition region between matter-enhanced and vacuum oscillations. The transition region is particularly sensitive to models of non-standard neutrino interactions and propagation. I examine several such non-standard models which predict a lower-energy transition region. I find that while several models provide a better fit to the solar neutrino data set, large experimental uncertainties lead to a low statistical significance.\nPart II describes the second analysis, where I look at neutron followers of contained atmospheric neutrino events in the SNO data set. These kinds of events are difficult backgrounds for nucleon decay measurements, and understanding the neutron follower multiplicity will allow for better rejection. It can also help improve measurements of the neutrino mass hierarchy and neutrino-nuclear cross sections. I find that the dependence of the average multiplicity on the visible energy agrees well with the predictions of simulations except for an unexplained deficit between 100 MeV and 600 MeV and an excess above 4 GeV. I determined the ability to distinguish neutrino and antineutrino events using the multiplicity by fitting for the double ratio $R \\equiv (\\overline{\\nu}/\\nu)_{\\text{data}} / (\\overline{\\nu}/{\\nu})_{\\text{MC}})$. I find $R = 0.93^{+0.91}_{-0.63}$ for a fit to a single multiplicity distribution per phase, and $R < 1.00$ for a fit to separate distributions for single electron ring, single muon ring, and multi-ring events. I also look at the agreement with a meson-exchange current cross section model developed to explain anomalous cross sections measured by MiniBooNE. Fitting for the strength of the MEC contribution as a fraction of the quasielastic charged-current cross section, I find an upper limit of $\\sigma_{MEC}/\\sigma_{QECC} < 0.17$ for a fit to combined distributions and $\\sigma_{MEC}/\\sigma_{QECC} < 0.04$ for a fit to separate distributions for ring count and type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".