  In the preparation for ESA's Euclid mission and the large amount of data it
will produce, we train deep convolutional neural networks on Euclid simulations
classify solar system objects from other astronomical sources. Using transfer
learning we are able to achieve a good performance despite our tiny dataset
with as few as 7512 images. Our best model correctly identifies objects with a
top accuracy of 94% and improves to 96% when Euclid's dither information is
included. The neural network misses ~50% of the slowest moving asteroids (v <
10 arcsec/h) but is otherwise able to correctly classify asteroids even down to
26 mag. We show that the same model also performs well at classifying stars,
galaxies and cosmic rays, and could potentially be applied to distinguish all
types of objects in the Euclid data and other large optical surveys.
