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H. Feldman (University of Kansas), R. Watkins (Willamette University), S.W. Chambers, A. Melott (University of Kansas)
We present a new method for the analysis of peculiar velocity surveys which removes contributions to velocities from small scale, nonlinear velocity modes while retaining information about large scale motions. Our method utilizes Karhunen-Loève methods of data compression to construct a set of moments out of the velocities which are minimally sensitive to small scale power. The set of moments are then used in a likelihood analysis. We develop criterion for the selection of moments, as well as a statistic to quantify the overall sensitivity of a set of moments to small scale power. Although we discuss our method in the context of peculiar velocity surveys, it may also prove useful in other situations where data filtering is required.
This work was supported in part by the National Science Foundation under grant number AST-0070702, the University of Kansas General Research Fund and the National Center for Supercomputing Applications.