2018MNRAS.481.3083F

E-science of photometric data requires automatic procedures and a precise recognition of periodic patterns to perform science as well as possible on large data. Analytical equations that enable us to set the best constraints to properly reduce processing time and hence optimize signal searches play a crucial role in this matter. These are increasingly important because the production of unbiased samples from variability indices and statistical parameters has not been achievable so far. We discuss the constraints used in periodic signals detection methods as well as the uncertainties in the estimation of periods and amplitudes. The frequency resolution necessary to investigate a time series is assessed with a new approach that estimates the necessary sampling resolution from shifts on the phase diagrams for successive frequency grid points. We demonstrate the underlying meaning of the oversampling factor. We reassess the frequency resolutions required to find the variability periods of EA stars and use the new resolutions to analyse a small sample of EA_up Catalina stars, i.e. EA stars previously classified as having insufficient number of observations at the eclipses. As a result, the variability periods of four EA_up stars were determined. Moreover, we have a new approach to estimate the amplitude and period variations. From these estimations, information about the intrinsic variations of the sources is obtained. For a complete characterization of the light-curve signal, the period uncertainty and period variation must be determined. Constraints on periodic signal searches were analysed and delimited.