Muon track reconstruction in a segmented bolometric array using multi-objective optimization

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