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Mechanical design of water cherenkov test experiment (WCTE) at CERN

2022· article· en· W4311170273 on OpenAlexaff
S V Garode, Satish Chinchanikar, C S Garde, A R Mache, Shardul Joshi, N Deshmukh, Punashri Patil, A Konaka, M Hartz

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsTRIUMF
Fundersnot available
KeywordsCherenkov radiationLarge Hadron ColliderPhotomultiplierUltrapure waterPipingGranularityDetectorCherenkov detectorPhysicsOpticsNuclear physicsMaterials scienceMechanical engineeringEngineeringComputer scienceNanotechnology

Abstract

fetched live from OpenAlex

The Water Cherenkov Test Experiment (WCTE) is an experiment proposed at CERN to measure the response of a Water Cherenkov Detector for charged particles such as π±, p+, e±, etc. The data obtained from WCTE will be used in future neutrino experiments. WCTE consists of a sealed cylindrical tank filled with ultrapure water. 128 multi-PhotoMultiplier Tubes (mPMTs) are mounted on a cylindrical support structure facing inwards to map out the Cherenkov radiation with high granularity. This work presents the mechanical design and analysis of the support structure for WCTE. It is designed to sustain the load of 128 mPMTs, arrangement of Photogrammetry system Cameras & lights and Calibration arm without significant change in the position / geometry of the structure. SS304 is identified as a suitable material to ensure the compatibility with the ultrapure water and Gd-loaded water. The structure is robust against stresses during handling and subsequent transport with and without water.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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