Pith. sign in

REVIEW 4 cited by

Towards a data-driven model of hadronization using normalizing flows

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.09296 v2 pith:KGMGNFQV submitted 2023-11-15 hep-ph hep-ex

Christian Bierlich , Phil Ilten , Tony Menzo , Stephen Mrenna , Manuel Szewc , Michael K. Wilkinson , Ahmed Youssef , Jure Zupan This is my paper · ORCID
classification hep-phhep-ex
keywords hadronizationmodeldistributionsexperimentalflowsintroducenormalizingtraining
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HDSense: An efficient method for ranking observable sensitivity

    hep-ph 2026-02 conditional novelty 6.0 of 10

    HDSense ranks observable subsets by adding per-observable Fisher information and penalizing overlap, picking near-optimal sets for Pythia hadronization parameters in tested cases.

  2. ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation

    hep-ph 2025-02 conditional novelty 6.0 of 10

    ARCANE reweighting adds a carefully designed, zero-average correction to event weights so that positive and negative pathways to the same event cancel, preserving all physical distributions.

  3. Herwig 7 with the Lund String Model: Tuning and Comparative Hadronization Studies

    hep-ph 2025-09 conditional novelty 5.0 of 10

    A Lund string model tune inside Herwig 7, the LH Tune, gives competitive descriptions of many LEP and LHC observables and enables fixed-shower comparison of string vs cluster hadronization.

  4. HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

    hep-ph 2025-12 conditional novelty 4.0 of 10

    HEPTAPOD uses LLM agents to drive FeynRules, MadGraph, Pythia, and analysis tools through schema-validated tool calls and run-card templates, demonstrated on a leptoquark signal scan.

Pith tools