  We introduce two new loss functions designed to directly optimise the
statistical significance of the expected number of signal events when training
neural networks to classify events as signal or background in the scenario of a
search for new physics at a particle collider. The loss functions are designed
to directly maximise commonly used estimates of the statistical significance,
$s/\sqrt{s+b}$, and the Asimov estimate, $Z_A$. We consider their use in a toy
SUSY search with 30~fb$^{-1}$ of 14~TeV data collected at the LHC. In the case
that the search for the SUSY model is dominated by systematic uncertainties, it
is found that the loss function based on $Z_A$ can outperform the binary cross
entropy in defining an optimal search region.
