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Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model

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arxiv 2411.02194 v2 pith:LSDKBYKM submitted 2024-11-04 hep-ph hep-ex

Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model

Nick Heller , Phil Ilten , Tony Menzo , Stephen Mrenna , Benjamin Nachman , Andrzej Siodmok , Manuel Szewc , Ahmed Youssef This is my paper
classification hep-ph hep-ex
keywords modelrejectionsamplingautodifferentiationhadronizationparameteradditionallyalgorithm
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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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Cited by 2 Pith papers

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    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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    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.