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Record W4384918873 · doi:10.48550/arxiv.2307.09689

Electron-beam Calibration of Aerogel Tiles for the HELIX RICH Detector

2023· preprint· en· W4384918873 on OpenAlexfundno aff
P. Allison, Marta Baiocchi, J. J. Beatty, L. Beaufore, D. H. Calderone, Yujie Chen, S. Coutu, E. Ellingwood, Noah Green, D. Hanna, H. B. Jeon, Rostom Mbarek, Keith McBride, I. Mognet, J. Musser, S. Nutter, S. O’Brien, N. Park, T. Rosin, M. Tabata, G. Tarlé, G. Visser, S. P. Wakely, M. Yu

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space AgencyNuclear Safety and Security CommissionMcGill UniversityTRIUMFNational Aeronautics and Space Administration
KeywordsCherenkov radiationDetectorParticle identificationPhysicsCherenkov detectorOpticsCosmic rayCalibrationNuclear physicsRadiator (engine cooling)ElectronBeam (structure)Refractive indexRange (aeronautics)Helix (gastropod)AerogelMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

The HELIX cosmic-ray detector is a balloon-borne instrument designed to measure the flux of light isotopes in the energy range from 0.2 GeV/n to beyond 3 GeV/n. It will rely on a ring-imaging Cherenkov (RICH) detector for particle identification at energies greater than 1 GeV/n and will use aerogel tiles with refractive index near 1.15 as the radiator. To achieve the performance goals of the experiment it is necessary to know the refractive index and its position dependence over the lateral extent of the tiles to a precision of O(10$^{-4}). In this paper we describe the apparatus and methods developed to calibrate the HELIX tiles in an electron beam, in order to meet this requirement.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.641

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 head0.061
GPT teacher head0.202
Teacher spread0.141 · 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 designSimulation or modeling
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

Explore more

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