  The Higgs boson is thought to provide the interaction that imparts mass to
the fundamental fermions, but while measurements at the Large Hadron Collider
(LHC) are consistent with this hypothesis, current analysis techniques lack the
statistical power to cross the traditional 5$\sigma$ significance barrier
without more data. \emph{Deep learning} techniques have the potential to
increase the statistical power of this analysis by \emph{automatically}
learning complex, high-level data representations. In this work, deep neural
networks are used to detect the decay of the Higgs to a pair of tau leptons. A
Bayesian optimization algorithm is used to tune the network architecture and
training algorithm hyperparameters, resulting in a deep network of eight
non-linear processing layers that improves upon the performance of shallow
classifiers even without the use of features specifically engineered by
physicists for this application. The improvement in discovery significance is
equivalent to an increase in the accumulated dataset of 25\%.
