A Trend Filtering Algorithm for wide field variability surveys

Abstract

We show that various systematics related to certain instrumental effects and data reduction anomalies in wide field variability surveys can be efficiently corrected by a Trend Filtering Algorithm (TFA) applied to the photometric time series produced by standard data pipelines. Statistical tests, performed on the database of the HATNet project, show that by the application of this filtering method the cumulative detection probability of periodic transits increases by up to 0.4 for variables brighter than 11 mag with a trend of increasing efficiency toward brighter magnitudes. We also show that TFA can be used for the reconstruction of periodic signals by iteratively filtering out systematic distortions.

12 pages with 12 figures and 4 tables, to appear in MNRAS

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