  Pulsar searches are computationally demanding efforts to discover dispersed
periodic signals in time- and frequency-resolved data from radio telescopes.
The complexity and computational expense of simultaneously determining the
frequency-dependent delay (dispersion) and the periodicity of the signal is
further exacerbated by the presence of various types of radio-frequency
interference (RFI) and observing-system effects. New observing systems with
wider bandwidths, higher bit rates and greater overall sensitivity (also to
RFI) further enhance these challenges. We present a novel approach to the
analysis of pulsar search data. Specifically, we present a neural-network-based
pipeline that efficiently suppresses a wide range of RFI signals and
instrumental instabilities and furthermore corrects for (a priori unknown)
interstellar dispersion. After initial training of the network, our analysis
can be run in real time on a standard desktop computer with a commonly
available, consumer-grade GPU. We complement our neural network with standard
algorithms for periodicity searches. In particular with the Fast Fourier
Transform (FFT) and the Fast Folding Algorithm (FFA) and demonstrate that with
these straightforward extensions, our method is capable of identifying even
faint pulsars, while maintaining an extremely low number of false positives. We
furthermore apply our analysis to a subset of the PALFA survey and demonstrate
that in most cases the automated dispersion removal of our network produces a
time series of similar quality as dedispersing using the actual dispersion
measure of the pulsar in question. On our test data we are able to make
predictions whether a pulsar is present in the data or not 200 times faster
than real time.
