  Most classification algorithms used in high energy physics fall under the
category of supervised machine learning. Such methods require a training set
containing both signal and background events and are prone to classification
errors should this training data be systematically inaccurate for example due
to the assumed MC model. To complement such model-dependent searches, we
propose an algorithm based on semi-supervised anomaly detection techniques,
which does not require a MC training sample for the signal data. We first model
the background using a multivariate Gaussian mixture model. We then search for
deviations from this model by fitting to the observations a mixture of the
background model and a number of additional Gaussians. This allows us to
perform pattern recognition of any anomalous excess over the background. We
show by a comparison to neural network classifiers that such an approach is a
lot more robust against misspecification of the signal MC than supervised
classification. In cases where there is an unexpected signal, a neural network
might fail to correctly identify it, while anomaly detection does not suffer
from such a limitation. On the other hand, when there are no systematic errors
in the training data, both methods perform comparably.
