Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model
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.
Authors
- Andrzej Siódmok (ORCID: https://orcid.org/0000-0001-9614-7856)
- Benjamin Nachman (ORCID: https://orcid.org/0000-0003-1024-0932)
- Manuel Szewc (ORCID: https://orcid.org/0000-0002-3014-5388)
- Noah Heller (ORCID: https://orcid.org/0009-0006-5422-6816)
- Tony Menzo (ORCID: https://orcid.org/0000-0002-2013-4570)
- Phil Ilten (ORCID: https://orcid.org/0000-0001-5534-1732)
- Stephen Mrenna
- Ahmed Youssef (ORCID: https://orcid.org/0000-0003-1585-4757)
Institutions
- Jagiellonian University (PL)
- Fermi National Accelerator Laboratory (US)
- National University of General San Martín (AR)
- International Center for Advanced Studies (AR)
Publication Details
- Journal
- arXiv (Cornell University)
- Published
- 2024-11-04
- DOI
- https://doi.org/10.48550/arxiv.2411.02194
- Citations
- 1
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article