MétaCan
Menu
Back to cohort
Record W3100932033

The Status and Initial Results of the MAJORANA DEMONSTRATOR Experiment

2017· article· en· W3100932033 on OpenAlexaff
N. Abgrall, T. Bode, V. Brudanin, M. Busch, M. Buuck, C. Cuesta, C. Dunagan, H. Ejiri, T. Gilliss, J. Gruszko, L. Hehn, R. Henning, R. Massarczyk, S. Mertens, J. Myslik, G. Othman, J. Rager, K. Rielage, B. Shanks, M. Shirchenko, D. Tedeschi, S. Vasilyev, K. Vetter, K. Vorren, C. Wiseman, W. Xu, E. Yakushev, V. Yumatov, I. Zhitnikov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsMAJORANADouble beta decayNeutrinoPhysicsParticle physicsDetectorSterile neutrinoScalabilityNuclear physicsLeptonNeutrino oscillationComputer scienceOperating systemOptics
DOInot available

Abstract

fetched live from OpenAlex

Neutrinoless double-beta decay searches play a major role in determining the nature of neutrinos, the existence of a lepton violating process, and the effective Majorana neutrino mass. The Majorana Collaboration assembled an array of high purity Ge detectors to search for neutrinoless double-beta decay in 76Ge. The Majorana Demonstrator is comprised of 44.1 kg (29.7 kg enriched in 76Ge) of Ge detectors divided between two modules contained in a low-background shield at the Sanford Underground Research Facility in Lead, South Dakota, USA. The initial goals of the Demonstrator are to establish the required background and scalability of a Ge-based next-generation ton-scale experiment. Following a commissioning run that started in 2015, the first detector module started low-background data production in early 2016. The second detector module was added in August 2016 to begin operation of the entire array. We discuss results of the initial physics runs, as well as the status and physics reach of the full Majorana Demonstrator experiment.

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: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.441

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.345
Teacher spread0.316 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicNeutrino Physics ResearchFrench-language works237,207