Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum. In this proceedings we make use of state of the art techniques to modernize the NNPDF methodology and study different models and optimizers in order to improve the quality of the PDF: improving both the quality and efficiency of the fits. We also present the evolutionary_keras library, a Keras implementation of the Evolutionary Algorithms used by NNPDF.

Studying the parton content of the proton with deep learning models / C. Juan, S. Carrazza, R. Stegeman. - In: POS PROCEEDINGS OF SCIENCE. - ISSN 1824-8039. - 372:(2021 Jan 28). ((Intervento presentato al convegno AISIS2019 Artificial Intelligence for Science, Industry and Society : October 21st - 25th tenutosi a Mexico City (México) nel 2019 [10.22323/1.372.0008].

Studying the parton content of the proton with deep learning models

C. Juan
Primo
;
S. Carrazza
Secondo
;
R. Stegeman
Ultimo
2021

Abstract

Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum. In this proceedings we make use of state of the art techniques to modernize the NNPDF methodology and study different models and optimizers in order to improve the quality of the PDF: improving both the quality and efficiency of the fits. We also present the evolutionary_keras library, a Keras implementation of the Evolutionary Algorithms used by NNPDF.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
10-lug-2020
Universidad Nacional Autónoma de México
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/735861
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