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Record W4323863031 · doi:10.48550/arxiv.2303.04698

'Searching for a needle in a haystack;' A Ba-tagging approach for an upgraded nEXO experiment

2023· preprint· en· W4323863031 on OpenAlexafffund
H. Rasiwala, K. Murray, Y. Lan, C. Chambers, M. Cvitan, T. Brunner, R. Collister, T. Daniels, R. Elmansali, W. Fairbank, R. Gornea, G. Gratta, T. Koffas, A. A. Kwiatkowski, K. G. Leach, A. Lennarz, C. Malbrunot, D. Ray, Raad Shaikh, L. Yang

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsNexen (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaMcGill UniversityUniversité de MontréalNational Science Foundation
KeywordsUpgradeHaystackDetectorPhysicsDouble beta decayNuclear physicsOpticsComputer science

Abstract

fetched live from OpenAlex

nEXO is a proposed experiment that will search for neutrinoless double-beta decay (0$νββ$) in 5-tonnes of liquid xenon (LXe), isotopically enriched in $^{136}$Xe. A technique called Ba-tagging is being developed as a potential future upgrade for nEXO to detect the $^{136}$Xe double-beta decay daughter isotope, $^{136}$Ba. An efficient Ba-tagging technique has the potential to boost nEXO's 0$νββ$ sensitivity by essentially suppressing non-double-beta decay background events. A conceptual approach for the extraction from the detector volume, trapping, and identification of a single Ba ion from 5 tonnes of LXe is presented, along with initial results from the commissioning of one of its subsystems, a quadrupole mass filter.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.277
GPT teacher head0.289
Teacher spread0.012 · 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 routes2
Has abstractyes

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