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Record W4389192767 · doi:10.22215/etd/2023-15814

Neck Alpha Background Rejection in DEAP-3600 using Pyrene and the Search for Superheavy Dark Matter

2023· dissertation· en· W4389192767 on OpenAlexaboutno aff
S. Garg

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsDark matterPhysicsWeakly interacting massive particlesWIMPDark energyScalar field dark matterAstrophysicsCosmology

Abstract

fetched live from OpenAlex

Modern dark matter experiments are looking for dark matter via its direct interaction with Standard model particles. DEAP-3600 (Dark matter Experiment using Argon Pulseshape discrimination) is a single-phase liquid argon (LAr)-based, direct detection dark matter experiment located 2 km underground at SNOLAB, Sudbury. DEAP is searching for dark matter via the elastic scattering of argon nuclei by dark matter particles. The Weakly Interacting Massive Particle (WIMP) is a prime candidate for dark matter. DEAP-3600 has currently published the leading limits on WIMP-nucleon spin-independent cross section on a LAr target of 3.9 x 10^(-45) cm^2 for a 100 GeV/c^2 WIMP mass at 90% confidence level. Acquiring high sensitivity for the rare, low-energy signals produced by DM-LAr interactions requires the minimization of background signals in the detector. A major source of background in DEAP is the alpha decays produced from radon progeny present in the acrylic material that composes the flow guides in the neck region of the detector. These "neck alpha" backgrounds are mitigated by coating the flow guides with a slow wavelength-shifting polymeric film - pyrene-doped polystyrene (PyPS). The long time-constant of PyPS helps reject neck alpha backgrounds using Pulse Shape Discrimination (PSD). A neck alpha background suppression factor of order (10^5) is shown to be achieved in this work. A measurement of the optical properties of neck components - including the surface roughness of acrylic, and the refractive index of PyPS composite are also described. In addition to being sensitive to WIMPs, DEAP-3600 is also sensitive to super-heavy dark matter candidates with masses up to the Planck scale. Dark matter with Planck-scale mass arises in well-motivated theories and could be produced by several cosmological mechanisms. This work presents the search for multi-scatter signals from this candidate particle in data collected over 813 days of live time with DEAP-3600. No signal events were observed leading to direct detection constraints for dark matter masses between 8.3 x 10^6 and 1.2 x 10^(19) GeV/c^2 and dark matter-Ar40 nucleus interaction cross-sections between 1 x 10^(-23) and 2.4 x 10^(-18) cm^2. These constraints are the first direct detection constraints on Planck-scale mass dark matter.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.295
Teacher spread0.270 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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