https://doi.org/10.1140/epjc/s10052-019-7197-2
Regular Article - Theoretical Physics
Towards a new generation of parton densities with deep learning models
TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Via Celoria 16, 20133, Milan, Italy
* e-mail: stefano.carrazza@cern.ch
Received:
15
July
2019
Accepted:
1
August
2019
Published online:
13
August
2019
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.
© The Author(s), 2019
