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Reweighting Monte Carlo Predictions and Automated Fragmentation Variations in Pythia 8
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This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model. This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight. This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice. Such explorations are important when determining the sensitivities of precision physics measurements. Accompanying code is available at https://gitlab.com/uchep/mlhad-weights-validation.
Forward citations
Cited by 4 Pith papers
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HDSense: An efficient method for ranking observable sensitivity
HDSense ranks observable subsets by adding per-observable Fisher information and penalizing overlap, picking near-optimal sets for Pythia hadronization parameters in tested cases.
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ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
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.
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Herwig 7 with the Lund String Model: Tuning and Comparative Hadronization Studies
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.
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The PYTHIA Facility
PYTHIA is presented as a 'big science facility' in software form: since 2018 its manuals drew ~9,600 citing works and ~47,000 unique authors across LHC, heavy-ion, flavor, astroparticle, and ML-for-physics communities.
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