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Record W7068057893

Neutron Measurements and Reactor Antineutrino Search with the SNO+ Detector in the Water Phase

2020· dissertation· en· W7068057893 on OpenAlexaff

Bibliographic record

VenueQSpace (Queen's University Library) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCalibrationDetectorMeasure (data warehouse)Neutron detectionOscillographNoise (video)
DOInot available

Abstract

fetched live from OpenAlex

The SNO+ experiment is a large-scale liquid scintillator neutrino experiment with a wide range of physics objectives. SNO+ has adopted a staged approach where the detector was first filled with ultra-pure water before substituting with liquid scintillator and the target isotope $^{130}$Te for neutrinoless double beta decay. During the SNO+ water phase, an $^{241}$Am$^{9}$Be source is deployed across the detector volume to calibrate the detector's energy response and its response to neutrons. The $^{241}$Am$^{9}$Be source emits a unique coincidence signal with the prompt event being a 4.4 MeV $\gamma$ and the delayed a neutron capture signal (2.2 MeV $\gamma$). A novel, minimalistic, statistical analysis of the $^{241}$Am$^{9}$Be calibration data was designed and used to measure the capture time constant $\tau$, capture cross-section $\sigma_{H,t}$, and the neutron detection efficiency $E_{\textrm{center}}$ at the center of the detector: \begin{equation} \begin{aligned} &\tau = 202.35 \pm 0.42\ (stat.)\ ^{+0.38}_{-0.31}\ (syst.)~\mu\textrm{s}, \\ &\sigma_{H,t} = 336.3 ^{+1.2}_{-1.5}~\textrm{mb}, \\ &E_{\textrm{center}} = (50.8 \pm 0.6)\%. \end{aligned} \end{equation} Additionally, with the help of Monte Carlo simulations, a volume-weighted neutron detection efficiency across the detector is evaluated to be $E_{\textrm{detector}} = (46.5 \pm 0.5\ (stat.\ only))\%$. The simulation is also central to an energy calibration using the 4.4 MeV $\gamma$ to measure the energy resolution and energy scale of the detector. Finally, with $\sim$115 days of early water data, an upper limit, $\hat{\Phi}_{\bar{\nu}_e, \textrm{ult}}$ = $(1.76 \pm 0.29) \times 10^6 \bar{\nu}/(\textrm{cm}^2\cdot\textrm{s})$, on the reactor antineutrino flux for SNO+ is obtained using a maximum likelihood approach. The limit is about a factor of 9 higher than the expected signal in SNO+, which can be calculated using available reactor output power data.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.236
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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