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Record W4412979014 · doi:10.1007/jhep07(2025)204

Power corrections to the production of a color-singlet final state in hadron collisions in the N-jettiness slicing scheme at NLO QCD

2025· article· en· W4412979014 on OpenAlexaff
Kirill Melnikov, Iván Pedron, Philip Pfohl

Bibliographic record

VenueJournal of High Energy Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersDeutsche Forschungsgemeinschaft
KeywordsPhysicsQuantum chromodynamicsLeptonParticle physicsHadronNuclear physics

Abstract

fetched live from OpenAlex

A bstract We compute next-to-leading power corrections in the zero-jettiness variable for the production of colorless final states at hadron colliders at next-to-leading order in QCD. To assess if the process-independence of leading power contributions can be extended, we attempt to construct generic expansions of phase spaces and matrix elements squared through next-to-leading power in the zero-jettiness. We highlight challenges associated with the collinear limit, where universality no longer holds at the subleading power, making the result process-dependent. We show that quantities that need to be calculated in the collinear limit can be obtained using Berends-Giele currents, enabling computation of power corrections to high-multiplicity final states. As a concrete example, we apply our method to compute power corrections in the zero-jettiness for lepton pair as well as multi-photon production in $$ q\overline{q} $$ q q ¯ collisions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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