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A generic `chirp’ of the form h(t) = A(t) cos(phi(t)) can be closely approximated by a connected set of multiscale chirplets with quadratically-evolving phase. The problem of finding the best approximation to a given signal using chirplets can be reduced to that of finding the path of minimum cost in a weighted, directed graph, and can be solved in polynomial time via dynamic programming. For a signal embedded in noise we apply constraints on the path length to obtain a statistic for detection of chirping signals in coloured noise. In this paper we present some results from using this test to detect binary black hole coalescences in simulated LIGO noise.

生成讲解博客的中文版本

生成AI智能校验的中文版本