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Energy response and position reconstruction in the DEAP-3600 dark matter experiment

2020· article· en· W2998932370 on OpenAlexaff
S. Langrock

Bibliographic record

VenueJournal of Physics Conference Series · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsSnolab
FundersScience and Technology Facilities Council
KeywordsWIMPPhysicsWeakly interacting massive particlesDark matterFiducial markerScatteringNuclear physicsParticle physicsDark energyScalar field dark matterOpticsAstrophysicsCosmologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Abstract DEAP-3600 is a dark matter WIMP (Weakly Interacting Massive Particles) search experiment, which aims to detect nuclear recoils from WIMP scattering in a liquid argon target. At WIMP masses of 100 GeV, DEAP-3600 has a projected sensitivity of 10−46 cm2 for the spin-independent elastic scattering cross section of WIMPs. The beta emissions from the intrinsic 39 Ar present in the natural Ar target, as well as external calibration sources, can be used to tune the energy calibration and position reconstruction in the energy region of interest (ROI) for WIMP signals. These proceedings will present the techniques used to determine the energy response and position reconstruction in DEAP-3600. Using these techniques, the light yield was found to be LY = (math) (fit syst.) ± 0.22(SPE syst.) PE/keVee with a fiducial mass for WIMP detection of 2 223 ± 74 kg for the first data set.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.224
Teacher spread0.210 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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