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Record W4323438860 · doi:10.17615/qjna-5v29

Multisite event discrimination for the majorana demonstrator

2023· article· en· W4323438860 on OpenAlexfundno aff
R. Henning, G. Othman, E. Yakushev, A.L. Reine, B.X. Zhu, Danielle Schaper, V. Yumatov, J. Rager, I.S. Guinn, K.J. Keeter, V.E. Guiseppe, A.W.P. Poon, R.L. Varner, W. Xu, M. Buuck, R.T. Kouzes, J. Myslik, J. Gruszko, C.J. Barton, V. Basu, E.W. Hoppe, S.I. Alvis, T.S. Caldwell, P.-H. Chu, L. Hehn, C. Wiseman, S.J. Meijer, B. Bos, D.C. Radford, N.W. Ruof, S. Mertens, R.J. Hegedus, M.A. Howe, S.I. Konovalov, C.R. Haufe, C.-H. Yu, S.R. Elliott, B.R. White, Y.-D. Chan, T. Gilliss, C.D. Christofferson, M. Shirchenko, J.F. Wilkerson, R. Massarczyk, J.A. Detwiler, Majorana Collaboration, M.P. Green, A.S. Barabash, B. Shanks, S. Vasilyev, W. Pettus, M.F. Kidd, C. Cuesta, G.K. Giovanetti, R.D. Martin, D. Tedeschi, D. Hervas Aguilar, A.M. Lopez, F.E. Bertrand, A. Piliounis, I.J. Arnquist, F.T. Avignone

Bibliographic record

VenueUNC Libraries · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersOak Ridge National LaboratoryNuclear PhysicsLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of CanadaPacific Northwest National LaboratoryNational Energy Research Scientific Computing CenterLaboratory Directed Research and DevelopmentU.S. Department of EnergyOffice of ScienceRussian Foundation for Basic ResearchNational Science Foundation
KeywordsMAJORANAEvent (particle physics)Computer sciencePhysicsNuclear physicsAstrophysicsNeutrino

Abstract

fetched live from OpenAlex

The Majorana Demonstrator is searching for neutrinoless double-beta decay (0νββ) in Ge76 using arrays of point-contact germanium detectors operating at the Sanford Underground Research Facility. Background results in the 0νββ region of interest from data taken during construction, commissioning, and the start of full operations have been recently published. A pulse shape analysis cut applied to achieve this result, named AvsE, is described in this paper. This cut is developed to remove events whose waveforms are typical of multisite energy deposits while retaining (90±3.5)% of single-site events. This pulse shape discrimination is based on the relationship between the maximum current and energy, and tuned using Th228 calibration source data. The efficiency uncertainty accounts for variation across detectors, energy, and time, as well as for the position distribution difference between calibration and 0νββ events, established using simulations.

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)0.002
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.258
Teacher spread0.239 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueUNC LibrariesSame topicRadiation Detection and Scintillator TechnologiesFrench-language works237,207