Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2411.02194

Authors: Heller, Nick, Ilten, Phil, Menzo, Tony, Mrenna, Stephen, Nachman, Benjamin, Siodmok, Andrzej, Szewc, Manuel, Youssef, Ahmed

Institutions: Lawrence Berkeley National Laboratory, University of Cincinnati, Fermi National Accelerator Laboratory, Jagiellonian University, National University of General San Martín, International Center for Advanced Studies

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Narodowym Centrum Nauki

Awards: Collaborative Research: CyberTraining: Pilot: Monte Carlo General Education Network (MCGEN), Elements: Automated parametric transformations for rapid particle physics simulations, Elements: Machine Learning Quark Hadronization, Discovering True Muonium

OpenAlex ID: W4404354941

Cites:

Cited by: 1

Related to: 10