Period detection in light curves from astronomical objects using correntropy

In this paper we propose a new method for determining the period in astronomical time series using correntropy, an information theoretical concept recently developed in the computational intelligence field. The time series correspond to the stellar brightness over time, so-called light curves, and are characterized as being noisy and unevenly sampled. The advantages of using correntropy instead of correlation are to escape from the constraints of linearity and Gaussianity and are clearly demonstrated. The performance of the proposed method is compared with other algorithms published in the literature on a set of light curves drawn from the MACHO survey. The results show that the correntropy-based method obtains the correct periods more frequently than the Lomb-Scargle periodogram and the Period04 program.