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[Paper Review] Cluster Hadronization in HERWIG 5.9

A. Kupčo|ArXiv.org|Jun 17, 1999
High-Energy Particle Collisions Research16 citations
TL;DR

This paper identifies a flaw in Herwig 5.9's cluster hadronization model where adding new baryon resonances unexpectedly reduces proton yield due to biased decay channel selection. To fix this, a new hadronization model is proposed that treats all decay channels uniformly, improving agreement with LEP $e^+e^-$ data—especially in the baryon sector—making it more reliable for HERA physics studies.

ABSTRACT

The HERWIG 5.9 cluster hadronization model is briefly discussed here. It is shown that the model has peculiar behaviour when new heavy baryon resonances are included in the HERWIG 5.9 particle table. New fragmentation model is proposed to cure this problem and simple tuning of HERWIG 5.9 with this new model has been made using event shapes variables and identified particle momentum spectra in electron-positron interactions at LEPI. Finally, the predictions of the two hadronization models are compared.

Motivation & Objective

  • Address the counterintuitive reduction in proton yield when new baryon resonances are added to Herwig 5.9’s particle table.
  • Identify the root cause: biased decay channel selection favoring certain phase space configurations over others.
  • Develop a new hadronization model that treats all decay channels equally to restore physical consistency.
  • Improve agreement with LEP $e^+e^-$ data, particularly for identified particle multiplicities and event shapes.
  • Enable more reliable predictions for baryon number propagation and strangeness suppression in HERA experiments.

Proposed method

  • Analyze the default Herwig 5.9 cluster hadronization algorithm (HWCHAD), focusing on decay channel selection via phase space weighting.
  • Identify that the algorithm assigns probabilities proportional to mean phase space, distorting decay branching ratios.
  • Propose a new model where all decay channels are treated with equal statistical weight, eliminating bias toward high-phase-space channels.
  • Implement the new model with consistent normalization of spin and mixing weights using the SWTEF array.
  • Tune the new model using LEP $e^+e^-$ event shape variables and identified particle momentum spectra.
  • Compare predictions of the new model (H5.9n) with the default Herwig 5.9 and experimental data from L3 and other LEP collaborations.

Experimental results

Research questions

  • RQ1Why does the inclusion of new baryon resonances in Herwig 5.9 lead to a decrease in predicted proton yield?
  • RQ2What is the origin of the bias in the default cluster hadronization model that distorts decay branching ratios?
  • RQ3How can a reweighted decay channel selection improve agreement with LEP $e^+e^-$ data, especially in the baryon sector?
  • RQ4To what extent does the new hadronization model improve predictions for identified particle multiplicities and event shapes?
  • RQ5Can the new model provide a more reliable description of baryon production for future HERA physics analyses?

Key findings

  • The default Herwig 5.9 cluster hadronization model underestimates proton and $Λ$ baryon yields when new resonances are included, contrary to expectations.
  • The root cause is a bias in decay channel selection: probabilities are proportional to mean phase space, favoring certain decays and suppressing others.
  • The new hadronization model, which treats all decay channels uniformly, restores physical consistency and improves agreement with data.
  • The new model (H5.9n) shows better agreement with LEP data than the default Herwig 5.9, particularly for $\pi^0$, $\pi^\pm$, $K^{0}$, $K^{\pm}$, and baryons like $p$, $\Lambda$, $\Sigma(1385)^{\pm}$.
  • The CAND1 and CAND2 tuning sets show that the new model reduces discrepancies in the baryon decuplet and improves $\eta^\prime$ and $\Phi(1020)$ predictions.
  • The new model provides a more reliable framework for HERA physics, especially for studies of baryon number propagation and strange diquark suppression.

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This review was created by AI and reviewed by human editors.