MétaCan
Menu
Back to cohort

Machine detector interface for the e+e− future circular collider

2019· article· en· W4412255088 on OpenAlexaff
M. Boscolo, Oscar Blanco-García, N. Bacchetta, Eleonora Belli, no-firstname benedikt, H. Burkhardt, G. Costa, K. Elsener, E. Leogrande, P. Janot, H. Ten Kate, Dima El Khechen, A. Kolano, R. Kersevan, M. Lueckhof, K. Oide, E. Perez, M. Dam

Bibliographic record

VenueResearch at the University of Copenhagen (University of Copenhagen) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsColliderInterface (matter)DetectorPhysicsParticle physicsComputer scienceNuclear physicsOperating systemTelecommunications

Abstract

fetched live from OpenAlex

The international Future Circular Collider (FCC) study [1] aims at a design of p-p, e + e -, e-p colliders to be built in a new 100 km tunnel in the Geneva region.The e + e -collider (FCC-ee) has a centre of mass energy range between 90 (Z-pole) and 375 GeV (t t).To reach such unprecedented energies and luminosities, the design of the interaction region is crucial.The crab-waist collision scheme [2] has been chosen for the design and it will be compatible with all beam energies.In this paper we will describe the machine detector interface layout including the solenoid compensation scheme.We will describe how this layout fulfills all the requirements set by the parameters table and by the physical constraints.We will summarize the studies of the impact of the synchrotron radiation, the analysis of trapped modes and of the backgrounds induced by single beam and luminosity effects giving an estimate of the losses in the interaction region and in the detector.

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)0.002
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0560.012

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 head0.257
Teacher spread0.237 · 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

Citations0
Published2019
Admission routes1
Has abstractno

Explore more

Same venueResearch at the University of Copenhagen (University of Copenhagen)Same topicParticle Detector Development and PerformanceFrench-language works237,207