  Recent advances in segmented solid-state detector arrays for rare-event
searches have allowed the technology to approach the ton-scale in detector mass
and the scale of meters in size. Often focused around searches for neutrinoless
double-beta decay or direct dark matter detection, such experiments also have
the capability to search for exotic particles that leave track-like signatures
across their volume. However, the segmented nature of such detector arrays
often sets the spatial resolution and makes the problem of reconstructing
track-like paths non-trivial. In this paper, we present an algorithm that
improves reconstruction of track-like events in segmented detectors using
multi-objective optimization - a computational technique that optimizes more
than one cost function at a time without specifying a quantitative weighting
between them. Such a technique allows the reconstruction of tracks through a
detector and the determination of path-lengths through individual elements.
When combined with the reconstructed energy depositions in each element this
allows for a calculation of the stopping power of track-like particles and
opens the door to searches for particles with abnormal stopping power like
monopoles or lightly-ionizing particles (LIPs). Results are presented which
evaluate the precision of the reconstruction tools as they currently stand
against Monte Carlo generated data. The algorithm is presented in the context
of the CUORE experiment, but has applications to other segmented calorimeter
detectors.
