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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

Nick Heller , Phil Ilten , Tony Menzo , Stephen Mrenna , Benjamin Nachman , Andrzej Siodmok , Manuel Szewc , Ahmed Youssef This is my paper · ORCID
classification hep-phhep-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 3 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. Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning

    physics.data-an 2025-07 conditional novelty 6.0 of 10

    Neural density estimators paired with nested sampling recover JUNO's energy response parameters with bias below 0.55% and uncertainties consistent with statistics, in simulation-based closure tests.

  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.

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