  Solar radio spikes are short lived, narrow bandwidth features in low
frequency solar radio observations. The timing of their occurrence and the
number of spikes in a given observation is often unpredictable. The high
temporal and frequency of resolution of modern radio telescopes such as NenuFAR
mean that manually identifying radio spikes is an arduous task. Machine
learning approaches to data exploration in solar radio data is on the rise.
Here we describe a convolutional neural network to identify the per pixel
location of radio spikes as well as determine some simple characteristics of
duration, spectral width and drift rate. The model, which we call SpikeNet, was
trained using an Nvidia Tesla T4 16GB GPU with ~100000 sample spikes in a total
time of 2.2 hours. The segmentation performs well with an intersection over
union in the test set of ~0.85. The root mean squared error for predicted spike
properties is of the order of 23%. Applying the algorithm to unlabelled data
successfully generates segmentation masks although the accuracy of the
predicted properties is less reliable, particularly when more than one spike is
present in the same 64 X 64 pixel time-frequency range. We have successfully
demonstrated that our convolutional neural network can locate and characterise
solar radio spikes in a number of seconds compared to the weeks it would take
for manual identification.
