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Record W4388927706 · doi:10.48550/arxiv.2311.12278

The Data Acquisition System for Phase-III of the BeEST Experiment

2023· preprint· en· W4388927706 on OpenAlexfundno aff
C. Bray, S. Fretwell, I. Kim, W. K. Warburton, F. Ponce, K. G. Leach, S. Friedrich, Ryan Abells, P. Amaro, Adrien Andoche, R. Cantor, David R. Diercks, Mauro Guerra, Angela Hall, Charles M. Harris, Jared D. Harris, L. Hayen, Paul-Antoine Hervieux, G. B. Kim, A. Lennarz, Vincenzo Lordi, Jorge Machado, P. Machule, A. D. Marino, David McKeen, X. Mougeot, C. Ruiz, Amit Samanta, J. P. Santos, Caitlyn Stone-Whitehead

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersEuropean Metrology Programme for Innovation and ResearchPacific Northwest National LaboratoryU.S. Department of EnergyGordon and Betty Moore FoundationLaboratory Directed Research and DevelopmentLawrence Livermore National LaboratoryNuclear PhysicsTRIUMF
KeywordsData acquisitionPixelDetectorPhase (matter)Computer scienceSampling (signal processing)Limit (mathematics)Computer hardwareMode (computer interface)Pulse (music)PhysicsElectronic engineeringOpticsArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

The BeEST experiment is a precision laboratory search for physics beyond the standard model that measures the electron capture decay of $^7$Be implanted into superconducting tunnel junction (STJ) detectors. For Phase-III of the experiment, we constructed a continuously sampling data acquisition system to extract pulse shape and timing information from 16 STJ pixels offline. Four additional pixels are read out with a fast list-mode digitizer, and one with a nuclear MCA already used in the earlier limit-setting phases of the experiment. We present the performance of the data acquisition system and discuss the relative advantages of the different digitizers.

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)0.002
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.049
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0490.018

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 head0.135
GPT teacher head0.242
Teacher spread0.107 · 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
GenreMethods

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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