  Many Standard Model extensions predict metastable massive particles that can
be detected by looking for displaced decay vertices in the inner detector
volume. Current approaches to search for these events in high-energy particle
collisions rely on the presence of additional energetic signatures to make an
online selection during data-taking, as the reconstruction of displaced
vertices is computationally intensive. Enabling trigger-level reconstruction of
displaced vertices could significantly enhance the reach of such searches.
  This work is a first step approximating the location of the primary vertex in
an idealised detector geometry using a 4-layer dense neural networks for
regression of the vertex location yielding a precision of $O(1\ \mathrm{mm})$
[$O(20\ \mathrm{mm})$] RMS in a low [high] track multiplicity environment.
