Data di Pubblicazione:
2019
Citazione:
Towards a new generation of parton densities with deep learning models / S. Carrazza, C. Juan. - In: THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS. - ISSN 1434-6044. - 79:8(2019 Aug 13). [10.1140/epjc/s10052-019-7197-2]
Abstract:
We present a new regression model for the determination of parton distribution functions (PDF) using techniques inspired from deep learning projects. In the context of the NNPDF methodology, we implement a new efficient computing framework based on graph generated models for PDF parametrization and gradient descent optimization. The best model configuration is derived from a robust cross-validation mechanism through a hyperparametrization tune procedure. We show that results provided by this new framework outperforms the current state-of-the-art PDF fitting methodology in terms of best model selection and computational resources usage.
Tipologia IRIS:
01 - Articolo su periodico
Elenco autori:
S. Carrazza, C. Juan
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