Jeffrey D. Scargle, research astrophysicist in the Planetary Systems Branch, Astrobiology and Space Science Division at NASA Ames Research Center, presents a practical algorithm for optimal segmentation analysis of event data.
Useful information from data sets consisting of points, or other measurements, distributed over a data space of dimension 1 or higher, can be extracted using data segmentation. A simple algorithm based on an old dynamic programming concept provides an effective and practical approach to such problems.
This non-parametric approach has many desired properties: no artificial limits on the scales or resolution of signals that can be recovered, objectivity, automation, elimination of noise while conserving the valid information in the data, adaptability to multivariate problems, and ability to incorporate variable instrumental efficiency and auxiliary information.
The algorithm is demonstrated on astronomical data, including gamma-ray data from the NASA Fermi Gamma Ray Space Telescope, the Sloan Digital Sky Survey of large scale structure of the Universe, and otherastronomical observatories.
Jeff Scargle graduated from Pomona College, Summa Cum Laude, majoring in astronomy, and received a Ph. D. in astrophysics from the California Institute of Technology. Following research and teaching positions in the University of California, Berkeley and Santa Cruz campuses, he joined the Space Science Division of the NASA Ames Research Center.
He is currently a member of the international collaboration behind the Fermi Gamma Ray Telescope, and has developed time series and other data analysis methods in wide use in high-energy (x-ray and gamma-ray) astronomy, extra-solar planetary detection, and elsewhere.