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J. D. Scargle (NASA Ames Research Center)
In astronomy as elsewhere the coming century is surely the Century of Data and Information Technology. This tutorial covers astrophysical information contained in time series data. I emphasize new methods to detect and characterize various signals in noisy data: periodicities, quasi-periodic oscillations, and aperiodic phenomena such as self-similar random or chaotic ("1/f noise") processes. New algorithms for time-domain and frequency-domain analysis will be reviewed, as will prospects for autonomous scientific discovery in time series.
I am grateful to Joe Bredekamp and the NASA Applied Information Systems Research Program for encouragement and support.
The author(s) of this abstract have provided an email address for comments about the abstract: jeffrey@sunshine.arc.nasa.gov