TL;DR
This paper introduces the NNPDF1.0 set of Parton Distribution Functions derived from a comprehensive DIS analysis using neural networks and Monte Carlo methods, providing a statistically robust uncertainty estimation.
Contribution
It is the first to combine neural networks with Monte Carlo sampling for PDF determination, enhancing the accuracy and reliability of uncertainty estimates.
Findings
Neural networks effectively interpolate parton distributions.
Monte Carlo sampling captures the full uncertainty in PDFs.
Preliminary phenomenological results demonstrate the set's applicability.
Abstract
We present the first NNPDF full set of Parton Distribution Functions from a comprehensive DIS analysis. This approach, combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as interpolating functions, provides a faithful and statistically sound determination of the uncertainty in parton distributions. The features of the fit and the results are discussed in details as well as some preliminary phenomenological analysis
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