A comparison of methods to characterize non-stationarity in time series data
POSTER
Abstract
In this work we examine methods to characterize a time-dependent noise background of time series data generated by the Laser Interferometer Gravitational wave Observatories (LIGO). This non-stationarity originates from both instrumental and environmental noise, and can be exhibited by sharp transient features in the data as well as by slowly-varying statistical properties. The efficient identification of the presence of non-stationarity can have the net effect of increasing the sensitivity of the detectors. We present the results of various methods we applied to characterize the non-stationarity of the data, including machine-learning approaches. These methods have exhibited significant overlap with results generated by standard LIGO data monitoring tools.
*The authors would like to acknowledge the support of the National Science Foundation for its funding of the LIGO observatories and the CGWA CREST grant, and the Center for Gravitational Wave Astronomy