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Record W2942891887

Design of a neutron calibration source for the SNO+ experiment

2018· dissertation· en· W2942891887 on OpenAlexaboutno aff
I. Semenec

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsCalibrationNeutron sourceNuclear engineeringNeutronNuclear physicsEnvironmental sciencePhysicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

SNO+ is a multipurpose detector situated at the SNOLAB facility located at Creighton mine
\n2 km deep. The SNO+ experiment will have three phases: water, pure scintillator and Te-loaded
\nscintillator. With the detector filled with scintillator, solar neutrinos, geo and reactor anti-neutrinos,
\nand supernova neutrinos can be studied. To analyze the data collected by the detector, it is
\nimportant to have detailed knowledge of the detector response. This is why calibration is a crucial
\npart of the experiment. The detector response to neutrons will allow us to study the anti-neutrino
\nflux coming from reactors in Canada. Anti-neutrinos can be detected via the inverse beta decay
\nreaction which can be tagged using the neutrons it produces.
\nThis thesis will discuss the radioactive calibration source Americium Beryllium (AmBe) which
\nproduces neutrons and gammas. The existing AmBe source - inherited from the SNO experiment
\n- that will be used in water phase has to be modified for the scintillator and loaded scintillator
\nphases. Simulations were carried out to determine the optimal additional shielding required for
\nthe scintillator phase. The optimal shielding was determined to be 2 mm of lead surrounded by
\n1 mm of stainless steel for the encapsulation. The new design for the AmBe source was finalised.
\nThe estimated neutron capture event detection efficiency is 74.22%. The analysis of the source
\ndeployment at various positions within the detector and the shadowing effects are discussed as
\nwell.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.256
Teacher spread0.237 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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