Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model
High Energy Physics - Phenomenology
2024-12-09 v2 High Energy Physics - Experiment
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.
Cite
@article{arxiv.2411.02194,
title = {Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model},
author = {Nick Heller and Phil Ilten and Tony Menzo and Stephen Mrenna and Benjamin Nachman and Andrzej Siodmok and Manuel Szewc and Ahmed Youssef},
journal= {arXiv preprint arXiv:2411.02194},
year = {2024}
}
Comments
12 pages, 5 figures