De-Trending Time Series for Astronomical Variability Surveys
People:
Yong-Ik Byun, Dae-Won Kim, Federica Bianco, Charles Alcock, Pavlos Protopapas.In this project, we have developed a de-trending algorithm for the removal of trends in time series. Trends in time series could be caused by various systematic and random noise sources such as cloud passages, telescope vibration or CCD noise. Those trends undermine the intrinsic signals of stars and should be removed. We determine the trends from subsets of stars that are highly correlated among themselves. These subsets are selected based on a hierarchical tree clustering algorithm. A bottom-up merging algorithm based on the departure from normal distribution in the correlation is developed to identify subsets, which we call clusters. After identification of clusters, we determine a trend per cluster by weighted sum of normalized light-curves. We then use a quadratic programming to de-trend all individual light-curves based on these determined trends. The developed algorithm can be applied to time series for trend removal in both narrow and wide field astronomy.
The develped pipeline is being used for variability detection in TAOS database.
☆ Download de-trending pipeline.
★ More details about algorithm.
Publications:
Last Updated at: 2009-01-05 02:22 PM.