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Multiple parton interactions in deep inelastic ep-scattering at HERA

2008· dissertation· en· W850232792 on OpenAlexaff
Sakar Osman

Bibliographic record

VenueDESY (CERN, DESY, Fermilab, IHEP, and SLAC) · 2008
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsHERAPhysicsPartonRapidityJet (fluid)Particle physicsDeep inelastic scatteringNuclear physicsHadronQuantum chromodynamicsScatteringTransverse planeInelastic scatteringOptics

Abstract

fetched live from OpenAlex

The production of jets with low transverse momenta (mini-jets) in deep inelastic electron-proton scattering is studied. The analyses uses data taken with the H1 detector at HERA during the years 1999 to 2000. The events are required to contain either at least one leading jet of Pt > 5 GeV (the inclusive 1-jet sample) or at least two hard jets where one of them has to be at an angle larger than 140 degrees with respect to the leading jet (inclusive 2-jet sample). Mini-jet multiplicities and their average transverse momenta are presented as a function of Q^2, in two regions of psuedo-rapidity and for two bins in the hadronic mass, W for the inclusive 1-jet sample. For the inclusive 2-jet sample the results are shown for direct and resolved photon interactions in two bins of W. The results are compared to various QCD based models. A new method for calibrating jet energy measurements up to 10 GeV has been developed and its performance has been studied.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.272
Teacher spread0.254 · 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
Published2008
Admission routes1
Has abstractyes

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