15 March 2012
Hameetman Auditorium, Cahill
2:30 PM

Characterizing light curves
Matthew Graham

Light curves can show tremendous variation in their temporal coverage,
sampling rates, errors and missing values, etc., which makes comparisons
between them difficult and training classifiers even harder. A common approach
to tackling this is to characterize a set of light curves via a set of common
features and then use this alternate homogeneous representation as the basis
for further analysis or training. Many different types of feature are used in
the literature to capture information contained in the light curve: moments,
flux and shape ratios, variability indices, periodicity measures, model
representations. The Caltech Time Series Characterization Service aims to
extract a comprehensive set of features from any supplied light curve -
currently over 60 features can be supplied. Vectors of such features derived
from the light curves of known classes of objects can then be used as the
training sets for particular supervised classifiers. In this talk, we will
review characterization features and discuss implementation details focused on
maximizing performance.