Keywords: tensor factorization, co-clustering, sensor network, pattern mining
Abstract: Ground-based gravitational-wave (GW) detectors are a frontier large-scale experiment in experimental astrophysics. Given the elusive nature of GWs, the ground-based detectors have complex interacting systems made up of exquisitely sensitive instruments which makes them susceptible to terrestrial noise sources. As these noise transients - termed as glitches - appear in the detector's main data channel, they can mask or mimic real GW signals resulting in false alarms in the detection pipelines. Given their high rate of occurrence compared to astrophysical signals, it is vital to examine these glitches and probe their origin in the detector's environment and instruments in order to possibly eliminate them from the science data. In this paper we present a tensor factorization-based data mining approach to finding witness events to these glitches in the network of heterogeneous sensors that monitor the detectors and build a catalog which can aid human operators in diagnosing the sources of these noise transients.