  After decades of space travel, low Earth orbit is a junkyard of discarded
rocket bodies, dead satellites, and millions of pieces of debris from
collisions and explosions. Objects in high enough altitudes do not re-enter and
burn up in the atmosphere, but stay in orbit around Earth for a long time. With
a speed of 28,000 km/h, collisions in these orbits can generate fragments and
potentially trigger a cascade of more collisions known as the Kessler syndrome.
This could pose a planetary challenge, because the phenomenon could escalate to
the point of hindering future space operations and damaging satellite
infrastructure critical for space and Earth science applications. As commercial
entities place mega-constellations of satellites in orbit, the burden on
operators conducting collision avoidance manoeuvres will increase. For this
reason, development of automated tools that predict potential collision events
(conjunctions) is critical. We introduce a Bayesian deep learning approach to
this problem, and develop recurrent neural network architectures (LSTMs) that
work with time series of conjunction data messages (CDMs), a standard data
format used by the space community. We show that our method can be used to
model all CDM features simultaneously, including the time of arrival of future
CDMs, providing predictions of conjunction event evolution with associated
uncertainties.
