  In this work, we characterize the performance of a deep convolutional neural
network designed to detect and quantify chemical elements in experimental X-ray
photoelectron spectroscopy data. Given the lack of a reliable database in
literature, in order to train the neural network we computed a large ($>$100 k)
dataset of synthetic spectra, based on randomly generated materials covered
with a layer of adventitious carbon. The trained net performs as good as
standard methods on a test set of $\approx$ 500 well characterized experimental
X-ray photoelectron spectra. Fine details about the net layout, the choice of
the loss function and the quality assessment strategies are presented and
discussed. Given the synthetic nature of the training set, this approach could
be applied to the automatization of any photoelectron spectroscopy system,
without the need of experimental reference spectra and with a low computational
effort.
