Speaker
Description
We analyzed a year-long muography dataset (2024–2025) acquired at the Sos Enattos mine in Sardinia, Italy, to investigate temporal variations in the overburden mass at the Sos Enattos site. This mine is a potential candidate site for the future European gravitational-wave detector, Einstein Telescope, and the characterization of its overburden is of major interest. Muography is a passive geophysical technique that exploits the attenuation of cosmic-ray muons to estimate the opacity of large geological structures. Muons are subatomic particles capable of traversing large amounts of matter. The flux of muons is measured along many distinct axes of observation, each corresponding to a specific trajectory through the overlying rock mass. Because muons are absorbed according to the amount of matter they encounter, changes in the measured flux along each axis can be interpreted as variations in overburden mass over time. This setup allows a single muon detector to investigate multiple regions simultaneously, providing spatially and temporally resolved information on overburden mass distribution.
A key aspect in analyzing muon time series is deciding how to group the signals from different trajectories to calculate the flux through distinct regions, since combining trajectories that are not coherent could mask meaningful variations. To address this, we applied a PCA-based multivariate analysis to jointly analyze the time series from all trajectories and identify spatially coherent regions characterized by common temporal behavior. This study demonstrates how muography, combined with PCA-based multivariate analysis, can be used to investigate the spatial organization and temporal variability of overburden mass at underground sites.