Publication Date:
2022
Citation:
Parton distribution functions / S. Forte, S. Carrazza - In: Artificial Intelligence For High Energy Physics / [a cura di] P. Calafiura, D. Rousseau, K. Terao. - [s.l] : World Scientific, 2022. - ISBN 978-981-12-3402-6. - pp. 715-762 [10.1142/9789811234033_0019]
abstract:
We discuss the determination of the parton substructure of hadrons by casting it as a peculiar form of pattern recognition problem in which the pattern is a probability distribution, and we present the way this
problem has been tackled and solved. Specifically, we review the NNPDF approach to PDF determination, which is based on the combination of a Monte Carlo approach with neural networks as basic underlying
interpolators. We discuss the current NNPDF methodology, based on genetic minimization, and its validation through closure testing. We then present recent developments in which a hyperoptimized deep-learning framework for PDF determination is being developed, optimized, and tested.
problem has been tackled and solved. Specifically, we review the NNPDF approach to PDF determination, which is based on the combination of a Monte Carlo approach with neural networks as basic underlying
interpolators. We discuss the current NNPDF methodology, based on genetic minimization, and its validation through closure testing. We then present recent developments in which a hyperoptimized deep-learning framework for PDF determination is being developed, optimized, and tested.
IRIS type:
03 - Contributo in volume
List of contributors:
S. Forte, S. Carrazza
Link to information sheet:
Book title:
Artificial Intelligence For High Energy Physics