  Gravitational-wave searches for cosmic strings are currently hindered by the
presence of detector glitches, some classes of which strongly resemble cosmic
string signals. This confusion greatly reduces the efficiency of searches. A
deep-learning model is proposed for the task of distinguishing between
gravitational wave signals from cosmic string cusps and simulated blip glitches
in design sensitivity data from the future Einstein Telescope. The model is an
ensemble consisting of three convolutional neural networks, achieving an
accuracy of 79%, a true positive rate of 76%, and a false positive rate of 18%.
This marks the first time convolutional neural networks have been trained on a
realistic population of Einstein Telescope glitches. On a dataset consisting of
signals and glitches, the model is shown to outperform matched filtering,
specifically being better at rejecting glitches. The behaviour of the model is
interpreted through the application of several methods, including a novel
technique called waveform surgery, used to quantify the importance of waveform
sections to a classification model. In addition, a method to visualise
convolutional neural network activations for one-dimensional time series is
proposed and used. These analyses help further the understanding of the
morphological differences between cosmic string cusp signals and blip glitches.
Because of its classification speed in the order of magnitude of milliseconds,
the deep-learning model is suitable for future use as part of a real-time
detection pipeline. The deep-learning model is transverse and can therefore
potentially be applied to other transient searches.
