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.
Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model
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abstract
We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model.
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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.