  In high-energy particle collisions, secondary decays can be reconstructed as
displaced vertices using the measured trajectories of charged particles. Such
vertices are useful in identifying and studying jets originating from $b$- or
$c$-hadrons, which is a key component of the physics programs of modern
collider experiments. While machine learning has become mainstream in particle
physics, most applications are on an per-object basis, for example the
prediction of class labels or the regression of object properties. However,
vertex reconstruction is a many-to-many problem, in which a set of input tracks
must be grouped into a second variable length set of vertices. In this work, we
propose a fully learned approach to reconstruct secondary vertices inside jets
based on recent advancements in object detection from computer vision. We
demonstrate and discuss the advantages of this approach, in particular its
ability to estimate the properties of any number of vertices, and conclude that
the same methodology could be applicable to other reconstruction tasks in
particle physics.
