  Most major discoveries in astronomy are unplanned, and result from surveying
the Universe in a new way, rather than by testing a hypothesis or conducting an
investigation with planned outcomes. For example, of the 10 greatest
discoveries made by the Hubble Space Telescope, only one was listed in its key
science goals. So a telescope that merely achieves its stated science goals is
not achieving its potential scientific productivity. Several next-generation
astronomical survey telescopes are currently being designed and constructed
that will significantly expand the volume of observational parameter space, and
should in principle discover unexpected new phenomena and new types of object.
However, the complexity of the telescopes and the large data volumes mean that
these discoveries are unlikely to be found by chance. Therefore, it is
necessary to plan explicitly for these unexpected discoveries in the design and
construction of the telescope. Two types of discovery are recognised:
unexpected objects, and unexpected phenomena. This paper argues that
next-generation astronomical surveys require an explicit process for detecting
the unexpected, and proposes an implementation of this process. This
implementation addresses both types of discovery, and relies heavily on
machine-learning techniques, and also on theory-based simulations that
encapsulate our current understanding of the Universe to compare with the data.
