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An integrated flux-symmetric spectrometer-magnet system for the SND@LHC experiment upgrade

2024· article· en· W4404962531 on OpenAlexaff
D. Abbaneo, S. Ahmad, R. Albanese, A. Alexandrov, F. Alicante, K. Androsov, A. Anokhina, Takashi Asada, C. Asawatangtrakuldee, M. Ayala-Torres, C. Battilana, A. Bay, Alcide Bertocco, C. Betancourt, D. Bick, R. Biswas, A. Blanco, V. Boccia, M. Bogomilov, D. Bonacorsi, W. Bonivento, P. Bordalo, Alexey Boyarsky, S. Buontempo, M. Campanelli, T. Camporesi, V. Canale, A. Castro, D. Centanni, F. Cerutti, M. Chernyavskiy, Ki-Young Choi, Serhii Cholak, F. Cindolo, M. Climescu, A. P. Conaboy, G. M. Dallavalle, Daniele Davino, P. De Bryas Dexmiers D‘archiac, G. De Lellis, M. de Magistris, A. De Roeck, A. De Rújula, D. De Simone, A. Di Crescenzo, D. Di Ferdinando, C. Dinç, R. Donà

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersScience and Technology Facilities Council
KeywordsLarge Hadron ColliderUpgradeConceptual designPhysicsMagnetSpectrometerNuclear physicsDetectorCalorimeter (particle physics)MuonCompact Muon SolenoidLuminosityNuclear engineeringPhase spaceComputer scienceOpticsEngineeringOperating system

Abstract

fetched live from OpenAlex

Abstract The proposed upgrade of the SND@LHC experiment for the High Luminosity phase of the LHC (HL-LHC) will strongly benefit from the presence of a magnetized region, allowing for muon momentum and charge measurement. In this paper we describe an iron core magnet system that is partly integrated with the calorimeter and that is designed to respect the strict constraints from the available space in the experimental cavern, power consumption, and field requirements. Semi-analytical tools are introduced to explore the parameter space, in order to define the primary design options. A full 3-D analysis is then performed in order to validate the optimal choice, and to propose a conceptual design, including sizing of the components, detector performances and stray fields. Several technical options are also discussed, anticipating the design phase.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.014
GPT teacher head0.293
Teacher spread0.280 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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