# Towards a new generation of parton densities with deep learning models

## TL;DR

This paper introduces a deep learning-inspired regression model for determining parton distribution functions, improving accuracy and efficiency over existing methods through a novel graph-based framework and hyperparameter tuning.

## Contribution

It presents a new deep learning-based framework for PDF determination that enhances model selection and computational efficiency within the NNPDF methodology.

## Key findings

- Outperforms current state-of-the-art PDF fitting methods.
- Uses a graph-generated model for PDF parametrization.
- Reduces computational resources needed for PDF fitting.

## 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.