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Record W4402226398 · doi:10.1103/physrevd.110.063006

Cosmic ray muons in laboratories deep underground

2024· article· en· W4402226398 on OpenAlexafffund
William Woodley, Anatoli Fedynitch, M.-C. Piro

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsUniversity of Alberta
FundersAlliance de recherche numérique du CanadaCanada First Research Excellence Fund
KeywordsMuonCosmic rayNuclear physicsPhysicsEnvironmental scienceAstronomy

Abstract

fetched live from OpenAlex

We provide comprehensive calculations of total muon fluxes, energy and angular spectra, and mean muon energies in deep underground laboratories—under flat overburdens and mountains and underwater—using our latest calculation code, v3. For precise modeling, we compiled rock densities and chemical compositions for various underground labs, as well as topographic map profiles of overburdens, and integrated them into our calculations. Our results show excellent agreement with available data for most underground sites when using the latest surface muon flux model, . Moreover, since our calculations do not rely on underground measurements of muons or other secondaries, we can verify the consistency of measurements across different detectors at different sites. is an open-source, publicly available program, providing a solid framework for accurate muon flux predictions in various underground environments, essential for applications in cosmic ray physics and dark matter searches. Published by the American Physical Society 2024

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.020
GPT teacher head0.459
Teacher spread0.439 · 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 designObservational
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

Citations9
Published2024
Admission routes2
Has abstractyes

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