  We present a new method to search for long transient gravitational waves
signals, like those expected from fast spinning newborn magnetars, in
interferometric detector data. Standard search techniques are computationally
unfeasible (matched filtering) or very demanding (sub-optimal semi-coherent
methods). We explored a different approach by means of machine learning
paradigms, to define a fast and inexpensive procedure. We used convolutional
neural networks to develop a classifier that is able to discriminate between
the presence or the absence of a signal. To complement the classification and
enhance its effectiveness, we also developed a denoiser. We studied the
performance of both networks with simulated colored noise, according to the
design noise curve of LIGO interferometers. We show that the combination of the
two models is crucial to increase the chance of detection. Indeed, as we
decreased the signal initial amplitude (from $10^{-22}$ down to $10^{-23}$) the
classification task became more difficult. In particular, we could not
correctly tag signals with an initial amplitude of $2 \times 10^{-23}$ without
using the denoiser. By studying the performance of the combined networks, we
found a good compromise between the search false alarm rate (2$\%$) and
efficiency (90$\%$) for a single interferometer. In addition, we demonstrated
that our method is robust with respect to changes in the power law describing
the time evolution of the signal frequency. Our results highlight the
computationally low cost of this method to generate triggers for long transient
signals. The study carried out in this work lays the foundations for further
improvements, with the purpose of developing a pipeline able to perform
systematic searches of long transient signals.
