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Record W3107359108 · doi:10.48550/arxiv.2012.02070

Parton color reconnection in Herwig 7 using a spacetime event topology

2020· preprint· en· W3107359108 on OpenAlexaff
Johannes Bellm, Cody B. Duncan, Stefan Gieseke, M. Myška, Andrzej Siódmok

Bibliographic record

VenueRepository KITopen (Karlsruhe Institute of Technology) · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsInstitute of Particle Physics
FundersNarodowym Centrum NaukiGrantová Agentura České RepublikyEuropean CommissionBundesministerium für Bildung und ForschungAustralian Government
KeywordsHadronizationSpacetime topologyPhysicsSpacetimePartonTopology (electrical circuits)Monte Carlo methodEvent (particle physics)Particle physicsEvent generatorQuantum chromodynamicsHadronQuantum mechanicsQuantum field theory in curved spacetimeQuantum gravity

Abstract

fetched live from OpenAlex

Herwig 7 is a general-purpose Monte Carlo generator of particle collisions comprising both hard perturbative as well as soft phenomenological physics. Herwig is therefore capable to describe the entire final state of hadronized particles in a collision event. A spacetime topology of a parton system entering hadronization is fully described and tested for the first time. A combination of information from particles momenta and spacetime positions is utilized to minimize a boost-invariant distance measure of the parton system. We present a reasonable agreement of the model with a selection of experimental data and conclude that spacetime event topology can be meaningfully used in the further development.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.321
Teacher spread0.289 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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