Contribution invited talk
A Monte Carlo analysis of nuclear PDFs with neural networks
While tremendous effort has been made to determine the parton distribution functions (PDFs) of a free proton, less is known about their modification in nuclei. Such information is vital for our understanding of parton dynamics, since it can provide valuable insight into nuclear effects that are not well understood. In this talk, I present nNNPDF1.0, the first analysis of nuclear PDFs from the NNPDF collaboration. Using Monte Carlo techniques together with powerful machine learning algorithms, we obtain a reliable estimation of nPDF central values and their uncertainties.