Minimisation strategies for the determination of parton density functions
Stefano Carrazza, Nathan P. Hartland
TL;DR
This paper reviews and compares different minimisation strategies, including CMA-ES and NGA, used in neural network-based determination of parton distribution and fragmentation functions, highlighting their effectiveness and differences.
Contribution
It provides a comparative analysis of CMA-ES and NGA algorithms for optimizing neural network fits of PDFs and FFs, offering insights into their relative performance.
Findings
CMA-ES performs comparably to NGA in PDF determination.
The paper highlights advantages of CMA-ES in certain optimization scenarios.
Comparison results inform future choices of minimisation strategies in PDF fitting.
Abstract
We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks. We present a short overview of a proton PDF determination obtained using the covariance matrix adaptation evolution strategy (CMA-ES) optimisation algorithm. We perform comparisons between the CMA-ES and the standard nodal genetic algorithm (NGA) adopted by the NNPDF collaboration.
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Taxonomy
TopicsParticle physics theoretical and experimental studies · High-Energy Particle Collisions Research · Quantum Chromodynamics and Particle Interactions