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arXiv · 1907.05075

Towards a new generation of parton densities with deep learning models

Stefano Carrazza·Juan Cruz-Martinez
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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.

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Stefano Carrazza, Juan Cruz-Martinez. 2019-07-11. Towards a new generation of parton densities with deep learning models. https://doi.org/10.1140/epjc%2Fs10052-019-7197-2

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