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Luminosity from thermal neutron counting with MPX detectors and relation to ATLAS reference luminosity at √<i>s</i>= 8 TeV proton-proton collisions

2017· article· en· W2755200985 on OpenAlexaff
A. Sopczak, B. Ali, N. A. Asbah, B. Bergmann, Khaled Bekhouche, D. Caforio, M. Campbell, E.H.M. Heijne, C. Leroy, A. Lipniacka, M. Nessi, S. Pospı́šil, F. Seifert, J. Šolc, P. Soueid, M. Suk, D. Tureček, Z. Vykydal

Bibliographic record

VenueJournal of Instrumentation · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversité de MontréalInstitute of Particle Physics
Fundersnot available
KeywordsPhysicsLuminosityNuclear physicsProtonDetectorAtlas (anatomy)AstrophysicsOptics

Abstract

fetched live from OpenAlex

A luminosity determination based on thermal neutron counting with six MPX silicon pixel devices installed in the ATLAS cavern is presented. Recently, the ATLAS Collaboration published final √s=8 TeV luminosity results. This made possible to perform a detailed comparison and verify the potential of the thermal neutron counting as a novel method for luminosity measurements to supplement the well-established presently used procedures. This measurement is unique to the MPX network and has the advantage that the neutrons, which pass the MPX devices, cannot result from activation processes of material nearby. Good agreement is found between the MPX neutron counting results and the ATLAS reference luminosity. The differences between the ATLAS and MPX luminosity measurements are described by a Gaussian distribution with width of 1.5%.

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)0.003
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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