Recent progress on NNPDF for LHC

TL;DR

This paper reviews recent advancements in NNPDF methodology for LHC data, highlighting the use of neural networks and Monte Carlo sampling to improve Parton Distribution Function determinations.

Contribution

It introduces a full DIS analysis of PDFs using neural networks and Monte Carlo methods, offering an unbiased and flexible approach compared to traditional techniques.

Findings

Neural networks serve as unbiased interpolators for PDFs.

Monte Carlo sampling captures the probability measure in PDF space.

The approach improves the flexibility and reliability of PDF fits.

Abstract

We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution Functions (PDFs). Our method is based on the idea of combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as unbiased universal interpolating functions. The general structure of the project and the features of the fit are described and compared to those of the traditional approaches.