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Abstract

The search for gravitational-wave signals in detector data is often hampered by the fact that many data analysis methods are based on the theory of stationary Gaussian noise, while actual measurement data frequently exhibit clear departures from these assumptions. Deriving methods from models more closely reflecting the data's properties promises to yield more sensitive procedures. The commonly used matched filter is such a detection method that may be derived via a Gaussian model. In this paper we propose a generalized matched filtering technique based on a Student-t distribution that is able to account for heavier-tailed noise and is robust against outliers in the data. On the technical side, it generalizes the matched-filter's least-squares method to an iterative, or adaptive, variation. In a simplified Monte Carlo study we show that when applied to simulated signals buried in actual interferometer noise it leads to a higher detection rate than the usual (Gaussian) matched filter.