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Record W2981811659 · doi:10.1063/1.5130980

Results on double beta decay of 82Se with CUPID-0 Phase I

2019· article· en· W2981811659 on OpenAlexaff
L. Pagnanini, O. Azzolini, J. W. Beeman, F. Bellini, M. Beretta, M. Biassoni, C. Brofferio, C. Bucci, S. Capelli, L. Cardani, P. Carniti, N. Casali, D. Chiesa, M. Clemenza, O. Cremonesi, A. Cruciani, I. Dafinei, S. Di Domizio, F. Ferroni, L. Gironi, A. Giuliani, P. Gorla, C. Gotti, G. Keppel, S.S. Nagorny, M. Nastasi, S. Nisi, C. Nones, D. Orlandi, M. Pallavicini, L. Pattavina, M. Pavan, G. Pessina, V. Pettinacci, S. Pirro, S. Pozzi, E. Previtali, A. Puiu, C. Rusconi, K. Schäffner, C. Tomei, M. Vignati, A. Zolotarova

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhysicsDouble beta decayBolometerDetectorNuclear physicsBETA (programming language)Phase (matter)Limit (mathematics)OpticsNeutrinoComputer scienceMathematics

Abstract

fetched live from OpenAlex

CUPID-0 is the first neutrinoless double beta decay (0νββ) experiment based on highly enriched scintillating bolometers. It consists in an array of Zn82Se detectors operated at LNGS since 2017. During the Phase I of data taking, concluded at the end of 2018, the experiment collected an exposure of 9.95 kg·yr of ZnSe. The simultaneous heat-light readout provides a powerful rejection of α-particles and allows us to suppress the background in the region of interest down to 3.5−0.9+1.0×10−3 counts/(keV·kg·y), an unprecedented level for this technique. Thanks to this achievement, we set the most stringent lower limit on the 82Se 0νββ half-life. Moreover, we have developed a full background model, which allows us to identify the origin of the events in the region of interest. The reconstruction of the CUPID-0 spectrum has enabled us to perform the most precise measurement of the 2νββ of 82Se.

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 score0.877
Threshold uncertainty score0.657

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 science0.0000.000
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 head0.035
GPT teacher head0.314
Teacher spread0.280 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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